OVERALL YEARLY ANALYSES
Plot of 100% MCP HRs against number of relocations
year <- read_csv("GM_Consolidated_ByYear.csv")
Missing column names filled in: 'X13' [13]Parsed with column specification:
cols(
Year = [32mcol_double()[39m,
Gila = [31mcol_character()[39m,
Sex = [31mcol_character()[39m,
Environment = [31mcol_character()[39m,
Home_Range_100mcp = [32mcol_double()[39m,
N100 = [32mcol_double()[39m,
Home_Range_95mcp = [32mcol_double()[39m,
N95 = [32mcol_double()[39m,
Home_Range_95kde = [32mcol_double()[39m,
N = [32mcol_double()[39m,
Home_Range_50kde = [32mcol_double()[39m,
N50 = [32mcol_double()[39m,
X13 = [33mcol_logical()[39m
)
# quick plot
# Graph1<-ggplot(year,aes(x=N100,y=Home_Range_100mcp,group=Environment))+
Graph1<-ggplot(year,aes(x=N100,y=Home_Range_100mcp))+
geom_point(aes(shape = factor(Environment)), size = 2)+
geom_smooth(aes(linetype=Environment),colour="black", method="lm") +
# scale_colour_manual(values=c(subsidized="cyan3",nonsubsidized="indian red1"))+
# labs(title = "100% MCP Home Ranges")+
xlab("Number of Relocations")+
ylab("100% MCP Area (ha)")+
# labs(caption = "Figure 3 | Non-Subsidized (Owl Head Buttes) vs. Subsidized (Stone Canyon) population 100% MCPs against number \n of fixes of the complete data set.")+
theme(plot.caption = element_text(hjust = 0,lineheight = 0.9))
# theme_bw()
Graph1<-Graph1+theme(axis.title=element_text(size = 14))
# legend at top-left, inside the plot
SCOH.hr.fig<-Graph1 + theme(legend.title = element_blank(),
legend.text = element_text(size = 12),
legend.justification=c(0,1),
legend.position=c(0.05, 0.95),
legend.background = element_blank(),
legend.key = element_blank(),
legend.box.background = element_rect(colour = "black")) +
scale_shape_discrete(name ="",
breaks=c("nonsubsidized", "subsidized"),
labels=c("Nonsubsidized", "Subsidized")) +
scale_linetype_discrete(name ="",
breaks=c("nonsubsidized", "subsidized"),
labels=c("Nonsubsidized", "Subsidized"))
SCOH.hr.fig

# dir.create("outputs") # create a new folder to hold the output files
# ggsave("outputs/SC_OHB_plot.pdf")
Overall combined 100% MCP means averaged across sex
library(Rmisc)
Means <- summarySE(year, measurevar="Home_Range_100mcp",
groupvars=c("Environment"),na.rm = TRUE)
kable(Means, format = "pandoc", caption = 'Overall combined 100% MCP means averaged across sex')
Overall combined 100% MCP means averaged across sex
| nonsubsidized |
26 |
33.44231 |
20.518658 |
4.0240400 |
8.287665 |
| subsidized |
53 |
10.40151 |
6.948743 |
0.9544832 |
1.915311 |
Overall combined 95% MCP means averaged across sex
Means.95mcp <- summarySE(year, measurevar="Home_Range_95mcp",
groupvars=c("Environment"),na.rm = TRUE)
Means.95mcp
Set projection for mapping
CRS.SC<-CRS("+proj=utm +zone=12 +ellps=WGS84 +units=m +no_defs")
Function for MCP analysis
Function of MCP polygons used for mapping
Function of KDE analysis
kde_analysis.href.plot <- function(filename, percentage){
data <- read.csv(file = filename)
x <- as.data.frame(data$EASTING)
y <- as.data.frame(data$NORTHING)
xy <- c(x,y)
data.proj <- SpatialPointsDataFrame(xy,data, proj4string = CRS.SC)
xy <- SpatialPoints(data.proj@coords)
kde<-kernelUD(xy, h="href", kern="bivnorm", grid=1000)
ver <- getverticeshr(kde, percentage)
area <- as.data.frame(round(ver$area,4))
.rowNamesDF(area, make.names=TRUE) <- data$LIZARDNUMBER
write.table(area,file="KDE_Hectares.csv",
append=TRUE,sep=",", col.names=FALSE, row.names=TRUE)
kde.points <- cbind((data.frame(data.proj@coords)),data$LIZARDNUMBER)
colnames(kde.points) <- c("x","y","lizardnumber")
kde.poly <- fortify(ver, region = "id")
units <- grid.text(paste(round(ver$area,2)," ha"), x=0.9, y=0.95,
gp=gpar(fontface=4, cex=0.9), draw = FALSE)
kde.plot <- ggplot() +
geom_polygon(data=kde.poly, aes(x=kde.poly$long, y=kde.poly$lat), alpha = 0.5) +
geom_point(data=kde.points, aes(x=x, y=y)) + theme_bw() +
labs(x="Easting (m)", y="Northing (m)", title=kde.points$lizardnumber) +
theme(legend.position="none", plot.title = element_text(face = "bold", hjust = 0.5)) +
annotation_custom(units)
kde.plot
}
Function of KDE polygons for mapping
kde_analysis.href.polygon <- function(filename, percentage){
data <- read.csv(file = filename)
x <- as.data.frame(data$EASTING)
y <- as.data.frame(data$NORTHING)
xy <- c(x,y)
data.proj <- SpatialPointsDataFrame(xy,data, proj4string = CRS.SC)
xy <- SpatialPoints(data.proj@coords)
kde<-kernelUD(xy, h="href", kern="bivnorm", grid=1000)
ver <- getverticeshr(kde, percentage)
ver@proj4string<-CRS.SC
area <- as.data.frame(round(ver$area,4))
.rowNamesDF(area, make.names=TRUE) <- data$YEAR
write.table(area,file="KDE_Hectares.csv",
append=TRUE,sep=",", col.names=FALSE, row.names=TRUE)
kde.points <- cbind((data.frame(data.proj@coords)),data$YEAR)
colnames(kde.points) <- c("x","y","year")
kde.poly <- fortify(ver, region = "id")
units <- grid.text(paste(round(ver$area,2)," ha"), x=0.9, y=0.95,
gp=gpar(fontface=4, cex=0.9), draw = FALSE)
ver
}
Function of raster of UD
# kde_analysis.href.raster <- function(filename){
# data <- read.csv(file = filename)
# x <- as.data.frame(data$EASTING)
# y <- as.data.frame(data$NORTHING)
# xy <- c(x,y)
# data.proj <- SpatialPointsDataFrame(xy,data, proj4string = CRS.SC)
# xy <- SpatialPoints(data.proj@coords)
# kde<-kernelUD(xy, h="href", kern="bivnorm", grid=1000)
# kde<-as(kde, "SpatialGridDataFrame")
# kde@proj4string<- CRS.SC
# kde
# }
Function of trajectory analysis and distance over time
traj_analysis <- function(filename){
relocs_data <- read.csv(file = filename)
relocs <- as.ltraj(cbind(relocs_data$EASTING, relocs_data$NORTHING),id=relocs_data$LIZARDNUMBER, typeII = FALSE, date=NULL)
relocs.df <- ld(relocs)
relocs_dist <- as.data.frame(sum(sapply(relocs.df$dist, sum, na.rm=TRUE)))
colnames(relocs_dist) <- "Total Distance"
name <- relocs.df$id[1]
row.names(relocs_dist) <- name
relocs_units <- grid.text(paste(round(relocs_dist,2),"m"), x=0.9, y=0.9,
gp=gpar(fontface=3, col="black", cex=0.9), draw = FALSE)
reloc.plot <- ggplot() + theme_classic() + geom_path(data=relocs.df, aes(x=x,y=y), linetype = "dashed", colour = "red",
arrow = arrow(length=unit(.5,"cm"), angle = 20, ends="last", type = "closed")) +
geom_point(data=relocs.df, aes(x=x, y=y)) + geom_point(data=relocs.df, aes(x=x[1],
y=y[1]), size = 3, color = "darkgreen", pch=0) +
labs(x="Easting (m)", y="Northing (m)", title=relocs.df$id[1]) +
theme(legend.position="none", plot.title = element_text(face = "bold", hjust = 0.5)) +
annotation_custom(relocs_units)
reloc.plot
}
Function of distance of time
dist_analysis <- function(filename){
relocs_data <- read.csv(file = filename)
relocs <- as.ltraj(cbind(relocs_data$EASTING, relocs_data$NORTHING),id=relocs_data$LIZARDNUMBER, typeII = FALSE, date=NULL)
relocs.df <- ld(relocs)
relocs_dist <- as.data.frame(sum(sapply(relocs.df$dist, sum, na.rm=TRUE)))
colnames(relocs_dist) <- "Total Distance"
name <- relocs.df$id[1]
row.names(relocs_dist) <- name
write.table(relocs_dist,file="reloc_dist.csv",
append=TRUE,sep=",", col.names=FALSE, row.names=TRUE)
dist.plot
}
Map of yearly HR shifts of a subset of Gila Monsters. Includes running MCP polygons, Fortify mcp polygons for ggplot2 by YEAR
M215_mcp.11<-mcp_analysis.POLY("./M215/2011 .csv", percentage= 100)
M215_mcp.12<-mcp_analysis.POLY("./M215/2012 .csv", percentage= 100)
F104_mcp.08<-mcp_analysis.POLY("./F104/2008 .csv", percentage= 100)
F104_mcp.09<-mcp_analysis.POLY("./F104/2009 .csv", percentage= 100)
F114_mcp.08<-mcp_analysis.POLY("./F114/2008 .csv", percentage= 100)
F114_mcp.09<-mcp_analysis.POLY("./F114/2009 .csv", percentage= 100)
F114_mcp.10<-mcp_analysis.POLY("./F114/2010 .csv", percentage= 100)
F114_mcp.11<-mcp_analysis.POLY("./F114/2011 .csv", percentage= 100)
F114_mcp.12<-mcp_analysis.POLY("./F114/2012 .csv", percentage= 100)
F137_mcp.09<-mcp_analysis.POLY("./F137/2009 .csv", percentage= 100)
F137_mcp.10<-mcp_analysis.POLY("./F137/2010 .csv", percentage= 100)
F137_mcp.11<-mcp_analysis.POLY("./F137/2011 .csv", percentage= 100)
F147_mcp.09<-mcp_analysis.POLY("./F147/2009 .csv", percentage= 100)
F147_mcp.10<-mcp_analysis.POLY("./F147/2010 .csv", percentage= 100)
F147_mcp.11<-mcp_analysis.POLY("./F147/2011 .csv", percentage= 100)
F147_mcp.12<-mcp_analysis.POLY("./F147/2012 .csv", percentage= 100)
F36_mcp.08<-mcp_analysis.POLY("./F36/2008 .csv", percentage= 100)
F36_mcp.09<-mcp_analysis.POLY("./F36/2009 .csv", percentage= 100)
F36_mcp.10<-mcp_analysis.POLY("./F36/2010 .csv", percentage= 100)
F36_mcp.11<-mcp_analysis.POLY("./F36/2011 .csv", percentage= 100)
F36_mcp.12<-mcp_analysis.POLY("./F36/2012 .csv", percentage= 100)
F66_mcp.08<-mcp_analysis.POLY("./F66/2008 .csv", percentage= 100)
F66_mcp.09<-mcp_analysis.POLY("./F66/2009 .csv", percentage= 100)
F66_mcp.10<-mcp_analysis.POLY("./F66/2010 .csv", percentage= 100)
M119_mcp.08<-mcp_analysis.POLY("./M119/2008 .csv", percentage= 100)
M119_mcp.09<-mcp_analysis.POLY("./M119/2009 .csv", percentage= 100)
M119_mcp.10<-mcp_analysis.POLY("./M119/2010 .csv", percentage= 100)
M112_mcp.07<-mcp_analysis.POLY("./M112/2007 .csv", percentage= 100)
M112_mcp.09<-mcp_analysis.POLY("./M112/2009 .csv", percentage= 100)
M112_mcp.10<-mcp_analysis.POLY("./M112/2010 .csv", percentage= 100)
M69_mcp.09<-mcp_analysis.POLY("./M69/2009 .csv", percentage= 100)
M69_mcp.10<-mcp_analysis.POLY("./M69/2010 .csv", percentage= 100)
## Fortify mcp polygons for ggplot2 *YEAR*:
F104_mcp.08T <- fortify(F104_mcp.08, region = "id")
F104_mcp.09T <- fortify(F104_mcp.09, region = "id")
F114_mcp.08T <- fortify(F114_mcp.08, region = "id")
F114_mcp.09T <- fortify(F114_mcp.09, region = "id")
F114_mcp.10T <- fortify(F114_mcp.10, region = "id")
F114_mcp.11T <- fortify(F114_mcp.11, region = "id")
F114_mcp.12T <- fortify(F114_mcp.12, region = "id")
F137_mcp.09T <- fortify(F137_mcp.09, region = "id")
F137_mcp.10T <- fortify(F137_mcp.10, region = "id")
F137_mcp.11T <- fortify(F137_mcp.11, region = "id")
F147_mcp.09T <- fortify(F147_mcp.09, region = "id")
F147_mcp.10T <- fortify(F147_mcp.10, region = "id")
F147_mcp.11T <- fortify(F147_mcp.11, region = "id")
F147_mcp.12T <- fortify(F147_mcp.12, region = "id")
F36_mcp.08T <- fortify(F36_mcp.08, region = "id")
F36_mcp.09T <- fortify(F36_mcp.09, region = "id")
F36_mcp.10T <- fortify(F36_mcp.10, region = "id")
F36_mcp.11T <- fortify(F36_mcp.11, region = "id")
F36_mcp.12T <- fortify(F36_mcp.12, region = "id")
F66_mcp.08T <- fortify(F66_mcp.08, region = "id")
F66_mcp.09T <- fortify(F66_mcp.09, region = "id")
F66_mcp.10T <- fortify(F66_mcp.10, region = "id")
M119_mcp.08T <- fortify(M119_mcp.08, region = "id")
M119_mcp.09T <- fortify(M119_mcp.09, region = "id")
M119_mcp.10T <- fortify(M119_mcp.10, region = "id")
M112_mcp.07T <- fortify(M112_mcp.07, region = "id")
M112_mcp.09T <- fortify(M112_mcp.09, region = "id")
M112_mcp.10T <- fortify(M112_mcp.10, region = "id")
M69_mcp.09T <- fortify(M69_mcp.09, region = "id")
M69_mcp.10T <- fortify(M69_mcp.10, region = "id")
M215_mcp.11T <- fortify(M215_mcp.11, region = "id")
M215_mcp.12T <- fortify(M215_mcp.12, region = "id")
mcp.shift.TEST4 <- ggplot() +
# geom_polygon(data=F104_mcp.08T, aes(x=F104_mcp.08T$long, y=F104_mcp.08T$lat),
# alpha=0.1,colour="black",linetype=2) +
# geom_polygon(data=F104_mcp.09T, aes(x=F104_mcp.09T$long, y=F104_mcp.09T$lat),
# alpha=0.1,colour="black",linetype=2) +
geom_polygon(data=F114_mcp.08T, aes(x=F114_mcp.08T$long, y=F114_mcp.08T$lat),
alpha=0.1,colour="black",linetype=3) +
geom_polygon(data=F114_mcp.09T, aes(x=F114_mcp.09T$long, y=F114_mcp.09T$lat),
alpha=0.1,colour="black",linetype=3) +
geom_polygon(data=F114_mcp.10T, aes(x=F114_mcp.10T$long, y=F114_mcp.10T$lat),
alpha=0.1,colour="black",linetype=3) +
geom_polygon(data=F114_mcp.11T, aes(x=F114_mcp.11T$long, y=F114_mcp.11T$lat),
alpha=0.1,colour="black",linetype=3) +
geom_polygon(data=F114_mcp.12T, aes(x=F114_mcp.12T$long, y=F114_mcp.12T$lat),
alpha=0.1,colour="black",linetype=3) +
geom_polygon(data=F137_mcp.09T, aes(x=F137_mcp.09T$long, y=F137_mcp.09T$lat),
alpha=0.1,colour="brown",linetype=4) +
geom_polygon(data=F137_mcp.10T, aes(x=F137_mcp.10T$long, y=F137_mcp.10T$lat),
alpha=0.1,colour="brown",linetype=4) +
geom_polygon(data=F137_mcp.11T, aes(x=F137_mcp.11T$long, y=F137_mcp.11T$lat),
alpha=0.1,colour="brown",linetype=4) +
geom_polygon(data=F147_mcp.09T, aes(x=F147_mcp.09T$long, y=F147_mcp.09T$lat),
alpha=0.1,colour="red",linetype=1) +
geom_polygon(data=F147_mcp.10T, aes(x=F147_mcp.10T$long, y=F147_mcp.10T$lat),
alpha=0.1,colour="red",linetype=1) +
geom_polygon(data=F147_mcp.11T, aes(x=F147_mcp.11T$long, y=F147_mcp.11T$lat),
alpha=0.1,colour="red",linetype=1) +
geom_polygon(data=F147_mcp.12T, aes(x=F147_mcp.12T$long, y=F147_mcp.12T$lat),
alpha=0.1,colour="red",linetype=1) +
# geom_polygon(data=F36_mcp.08T, aes(x=F36_mcp.08T$long, y=F36_mcp.08T$lat),
# alpha=0.1,colour="black",linetype=6) +
# geom_polygon(data=F36_mcp.09T, aes(x=F36_mcp.09T$long, y=F36_mcp.09T$lat),
# alpha=0.1,colour="black",linetype=6) +
# geom_polygon(data=F36_mcp.10T, aes(x=F36_mcp.10T$long, y=F36_mcp.10T$lat),
# alpha=0.1,colour="black",linetype=6) +
# geom_polygon(data=F36_mcp.11T, aes(x=F36_mcp.11T$long, y=F36_mcp.11T$lat),
# alpha=0.1,colour="black",linetype=6) +
# geom_polygon(data=F36_mcp.12T, aes(x=F36_mcp.12T$long, y=F36_mcp.12T$lat),
# alpha=0.1,colour="black",linetype=6) +
geom_polygon(data=F66_mcp.08T, aes(x=F66_mcp.08T$long, y=F66_mcp.08T$lat),
alpha=0.1,colour="green",linetype=5) +
geom_polygon(data=F66_mcp.09T, aes(x=F66_mcp.09T$long, y=F66_mcp.09T$lat),
alpha=0.1,colour="green",linetype=5) +
geom_polygon(data=F66_mcp.10T, aes(x=F66_mcp.10T$long, y=F66_mcp.10T$lat),
alpha=0.1,colour="green",linetype=5) +
geom_polygon(data=M119_mcp.08T, aes(x=M119_mcp.08T$long, y=M119_mcp.08T$lat),
alpha=0.1,colour="blue",linetype=6) +
geom_polygon(data=M119_mcp.09T, aes(x=M119_mcp.09T$long, y=M119_mcp.09T$lat),
alpha=0.1,colour="blue",linetype=6) +
geom_polygon(data=M119_mcp.10T, aes(x=M119_mcp.10T$long, y=M119_mcp.10T$lat),
alpha=0.1,colour="blue",linetype=6) +
geom_polygon(data=M112_mcp.07T, aes(x=M112_mcp.07T$long, y=M112_mcp.07T$lat),
alpha=0.1,colour="purple",linetype=2) +
geom_polygon(data=M112_mcp.09T, aes(x=M112_mcp.09T$long, y=M112_mcp.09T$lat),
alpha=0.1,colour="purple",linetype=2) +
geom_polygon(data=M112_mcp.10T, aes(x=M112_mcp.10T$long, y=M112_mcp.10T$lat),
alpha=0.1,colour="purple",linetype=2) +
# geom_polygon(data=M69_mcp.09T, aes(x=M69_mcp.09T$long, y=M69_mcp.09T$lat),
# alpha=0.1,colour="black") +
# geom_polygon(data=M69_mcp.10T, aes(x=M69_mcp.10T$long, y=M69_mcp.10T$lat),
# alpha=0.1,colour="black") +
# geom_polygon(data=M215_mcp.11T, aes(x=M215_mcp.11T$long, y=M215_mcp.11T$lat),
# alpha=0.1,colour="black") +
# geom_polygon(data=M215_mcp.12T, aes(x=M215_mcp.12T$long, y=M215_mcp.12T$lat),
# alpha=0.1,colour="black") +
theme_bw() +labs(x="Easting (m)", y="Northing (m)") +
labs(caption = "Figure 5 | SC Yearly home range shifts of 8 lizards, both males and females. Home range shifts appear to be \n relativley stable over study years.")+
theme(plot.caption = element_text(hjust = 0,lineheight = 0.9))
# theme(legend.position="none", plot.title = element_text(face = "bold", hjust = 0.5))
## within each geom_polygon line?:
## aes(colour="red"or"M112_mcp.09T")...+scale_color_manual(name="",breaks=c("","",...""))+
## values=c(""="",...)
mcp.shift.TEST4
Raw group 100% MCP home range means of Stone Canyon and Owl Head Buttes. Grouped by environment and sex
library(Rmisc)
YR_GRP_Means <- summarySE(year, measurevar="Home_Range_100mcp",
groupvars=c("Environment","Sex"),na.rm = TRUE)
kable(YR_GRP_Means, format = "pandoc",
caption = 'Table 1 | Raw group 100% MCP home range means of Stone Canyon and Owl Head Buttes. Grouped by environment and sex.')
Raw group 95% MCP home range means of Stone Canyon and Owl Head Buttes. Grouped by environment and sex
YR_GRP_Means95 <- summarySE(year, measurevar="Home_Range_95mcp",
groupvars=c("Environment","Sex"),na.rm = TRUE)
kable(YR_GRP_Means95, format = "pandoc", caption = 'Table 2 | Raw group 95% MCP home range means of raw data of Stone Canyon and Owl Head Buttes. Grouped by environment and sex.')
RM-ANOVA for 100% MCP analyses between the subsidized and non-subsidized
# Get p-values from mixed model F values:
library(lme4)
library(readr)
year <- read_csv("GM_Consolidated_ByYear.csv")
RMmod.year<-lmer(Home_Range_100mcp~Environment+Year+Sex+N100+Environment*Sex+
(1|Gila),data = year)
summary(RMmod.year)
ANOVA table for 100% MCPs between the two populations
anova(RMmod.year)
RM-ANOVA for 95% MCP analyses between the subsidized and non-subsidized
RMmod.year95<-lmer(Home_Range_95mcp~Environment+Year+Sex+N100+Environment*Sex+
(1|Gila),data = year)
summary(RMmod.year95)
ANOVA table for 95% MCPs between the two populations
anova(RMmod.year95)
Raw group means AND adjusted EMMs of Yearly Overall 100%MCP

Directional means of home range (100% MCP) after being adjusted for year, sex and sample size
kable(ref_dfRM_sex, format = "pandoc", caption = 'Table | Directional means of home range (100% MCP) after being adjusted for year, sex and sample size.')
RM-ANOVA of 95% KDEs for the subsidized population
RM.KDEmod.year<-lmer(Home_Range_95kde~Year+Sex+N+(1|Gila),data = sub)
summary(RM.KDEmod.year)
ANOVA Table for 95%KDE
anova(RM.KDEmod.year)
RM-ANOVA of 50% KDEs for the subsidized
RM.KDE.50.mod.year<-lmer(Home_Range_50kde~Year+Sex+N+(1|Gila),data = sub)
summary(RM.KDE.50.mod.year)
ANOVA Talbe of 50% KDE for the subsidized
anova(RM.KDE.50.mod.year)
TABLE. Raw Group 50% KDE home range means male and female home ranges at Stone Canyon
YR_GRP_Means.50KDE <- summarySE(sub, measurevar="Home_Range_50kde",
groupvars=c("Sex"),na.rm = TRUE)
kable(YR_GRP_Means.50KDE, format = "pandoc", caption = 'Table 5 | Raw Group 50% KDE home range means male and female home ranges at Stone Canyon.')
Raw group means AND adjusted EMMs of Yearly Overall 95% KDEs between non/subsidized populations

Collective grid of 100% MCP and 95% KDE of both sites from above
ggarrange(Raw.YearHR, yr.mean.adj, Raw.kde, kde.mean.adj, labels = c("A", "B", "C","D"),
ncol = 2, nrow = 2)

43.4 male 42.9 female Yearly overall means of 95% KDEs grouped by site and sex
YR_Means.95KDEall <- summarySE(year, measurevar="Home_Range_95kde",
groupvars=c("Environment","Sex"),na.rm = TRUE)
kable(YR_Means.95KDEall, format = "pandoc", caption = 'Table | Raw Group 95% KDE home range means male and female home ranges at non/subsidized.')
Table | Raw Group 95% KDE home range means male and female home ranges at non/subsidized.
| nonsubsidized |
female |
5 |
36.80000 |
9.603905 |
4.294997 |
11.924824 |
| nonsubsidized |
male |
6 |
69.40000 |
27.763789 |
11.334520 |
29.136310 |
| subsidized |
female |
37 |
22.98892 |
11.046272 |
1.815996 |
3.683010 |
| subsidized |
male |
13 |
35.00308 |
12.057546 |
3.344161 |
7.286302 |
Pairwise Comparisons, between sexes by environment, and between environments averaged across sex
RMmod.year.Em<-lmer(Home_Range_100mcp~Environment+Year+Sex+N100+Environment*Sex+
(1|Gila),data = year)
# RMmod.year.Em95<-lmer(Home_Range_95mcp~Environment+Year+Sex+N95+Environment*Sex+
# (1|Gila),data = year)
# Sex.emm.oa <- emmeans(RMmod.year.Em, c("Environment","Sex"))
# pairs(Sex.emm.oa)
emm_s.t2 <- emmeans(RMmod.year.Em, pairwise ~ Sex | Environment)
emm_s.t2
# emm_s.e1 <- emmeans(RMmod.year.Em, pairwise ~ Environment)
# emm_s.e1
Graphical Comparisons of Sex Within Each Environment:
plot(emm_s.t2, comparisons = TRUE, xlab = "Least Square Mean (ha)", ylab = "Environment")
Pairwise by sex between enviornements 100% MCP, and 95% KDEs
emm_s.t3 <- emmeans(RMmod.year.Em, pairwise ~ Environment | Sex)
emm_s.t3
# emm_s.t95 <- emmeans(RMmod.year.Em95, pairwise ~ Environment | Sex)
# emm_s.t95
Graphical Comparisons of Sex between the two populations:
plot(emm_s.t3, comparisons = TRUE, xlab = "Least Square Mean (ha)", ylab = "Environment")
Ineractive map of MCPs at Stone Canyon
M67_MCP<-mcp_analysis.POLY('./M67/M67 .csv', percentage= 100)
M69_MCP<-mcp_analysis.POLY('./M69/M69 .csv', percentage= 100)
M255_MCP<-mcp_analysis.POLY('./M255/M255 .csv', percentage= 100)
M215_MCP<-mcp_analysis.POLY('./M215/M215 .csv', percentage= 100)
M14_MCP<-mcp_analysis.POLY('./M14/M14 .csv', percentage= 100)
M119_MCP<-mcp_analysis.POLY('./M119/M119 .csv', percentage= 100)
M112_MCP<-mcp_analysis.POLY('./M112/M112 .csv', percentage= 100)
F66_MCP<-mcp_analysis.POLY('./F66/F66 .csv', percentage= 100)
F36_MCP<-mcp_analysis.POLY('./F36/F36 .csv', percentage= 100)
F252_MCP<-mcp_analysis.POLY('./F252/F252 .csv', percentage= 100)
F214_MCP<-mcp_analysis.POLY('./F214/F214 .csv', percentage= 100)
F200_MCP<-mcp_analysis.POLY('./F200/F200 .csv', percentage= 100)
F147_MCP<-mcp_analysis.POLY('./F147/F147 .csv', percentage= 100)
F146_MCP<-mcp_analysis.POLY('./F146/F146 .csv', percentage= 100)
F137_MCP<-mcp_analysis.POLY('./F137/F137 .csv', percentage= 100)
F135_MCP<-mcp_analysis.POLY('./F135/F135 .csv', percentage= 100)
F114_MCP<-mcp_analysis.POLY('./F114/F114 .csv', percentage= 100)
F104_MCP<-mcp_analysis.POLY('./F104/F104 .csv', percentage= 100)
Male.MCP <- rbind(M67_MCP,M69_MCP,M255_MCP,M215_MCP,M14_MCP,M119_MCP,M112_MCP)
Female.MCP <- rbind(F66_MCP,F36_MCP,F252_MCP,F214_MCP,F200_MCP,F147_MCP,F146_MCP,F137_MCP,
F135_MCP,F114_MCP,F104_MCP)
mapviewOptions(basemaps = c("OpenStreetMap","Esri.WorldImagery","OpenTopoMap"),
na.color = "magenta",
layers.control.pos = "topleft")
mapview(Male.MCP, legend=F, zcol="id", col.regions = c("blue"), alpha.regions=0.3) +
mapview(Female.MCP, legend=F, zcol = "id", col.regions = c("red"), alpha.regions=0.3)
Create stagnant stamen map of MCPs at Stone Canyon
Error: Don't know how to add north2(SC_stamen_map, x = 0.89, y = 0.85, scale = 0.1, symbol = 16) to a plot

Interactive map of KDEs at Stone Canyon
M67_KDE<-kde_analysis.href.polygon('./M67/M67 .csv', percentage= 95)
M69_KDE<-kde_analysis.href.polygon('./M69/M69 .csv', percentage= 95)
M255_KDE<-kde_analysis.href.polygon('./M255/M255 .csv', percentage= 95)
M215_KDE<-kde_analysis.href.polygon('./M215/M215 .csv', percentage= 95)
M14_KDE<-kde_analysis.href.polygon('./M14/M14 .csv', percentage= 95)
M119_KDE<-kde_analysis.href.polygon('./M119/M119 .csv', percentage= 95)
M112_KDE<-kde_analysis.href.polygon('./M112/M112 .csv', percentage= 95)
F66_KDE<-kde_analysis.href.polygon('./F66/F66 .csv', percentage= 95)
F36_KDE<-kde_analysis.href.polygon('./F36/F36 .csv', percentage= 95)
F252_KDE<-kde_analysis.href.polygon('./F252/F252 .csv', percentage= 95)
F214_KDE<-kde_analysis.href.polygon('./F214/F214 .csv', percentage= 95)
F200_KDE<-kde_analysis.href.polygon('./F200/F200 .csv', percentage= 95)
F147_KDE<-kde_analysis.href.polygon('./F147/F147 .csv', percentage= 95)
F146_KDE<-kde_analysis.href.polygon('./F146/F146 .csv', percentage= 95)
F137_KDE<-kde_analysis.href.polygon('./F137/F137 .csv', percentage= 95)
F135_KDE<-kde_analysis.href.polygon('./F135/F135 .csv', percentage= 95)
F114_KDE<-kde_analysis.href.polygon('./F114/F114 .csv', percentage= 95)
F104_KDE<-kde_analysis.href.polygon('./F104/F104 .csv', percentage= 95)
Male.KDE <- rbind(M67_KDE,M69_KDE,M255_KDE,M215_KDE,M14_KDE,M119_KDE,M112_KDE)
Female.KDE <- rbind(F66_KDE,F36_KDE,F252_KDE,F214_KDE,F200_KDE,F147_KDE,F146_KDE,F137_KDE,
F135_KDE,F114_KDE,F104_KDE)
mapviewOptions(basemaps = c("OpenStreetMap","Esri.WorldImagery","OpenTopoMap"),
na.color = "magenta",
layers.control.pos = "topleft")
mapview(Male.KDE, legend=F, zcol="id", col.regions = c("blue"), alpha.regions=0.3) +
mapview(Female.KDE, legend=F, zcol = "id", col.regions = c("red"), alpha.regions=0.3)
TABLE
Table | Subsidized and non-subsidized directional means of KDE home ranges after being adjusted for year, sex and sample size.
| nonsubsidized |
female |
42.44837 |
9.346459 |
53.01148 |
23.70185 |
61.19490 |
| subsidized |
female |
20.93992 |
4.083131 |
22.92042 |
12.49169 |
29.38814 |
| nonsubsidized |
male |
80.81873 |
9.084540 |
52.50968 |
62.59348 |
99.04398 |
| subsidized |
male |
35.27439 |
5.501543 |
29.24014 |
24.02648 |
46.52229 |
SEASONAL ANALYSES
Map of seasonal fluctions of home ranges
## Create MCP polygons by SEASON:
M215_mcp.EM<-mcp_analysis.POLY("./M215/Emergence .csv", percentage= 100)
M215_mcp.DRY<-mcp_analysis.POLY("./M215/Dry .csv", percentage= 100)
M215_mcp.MON<-mcp_analysis.POLY("./M215/Monsoon .csv", percentage= 100)
M112_mcp.DRY<-mcp_analysis.POLY("./M112/Dry .csv", percentage= 100)
M112_mcp.MON<-mcp_analysis.POLY("./M112/Monsoon .csv", percentage= 100)
M112_mcp.PM<-mcp_analysis.POLY("./M112/Post_Monsoon .csv", percentage= 100)
M119_mcp.DRY<-mcp_analysis.POLY("./M119/Dry .csv", percentage= 100)
M119_mcp.MON<-mcp_analysis.POLY("./M119/Monsoon .csv", percentage= 100)
M119_mcp.PM<-mcp_analysis.POLY("./M119/Post_Monsoon .csv", percentage= 100)
F114_mcp.EM<-mcp_analysis.POLY("./F114/Emergence .csv", percentage= 100)
F114_mcp.DRY<-mcp_analysis.POLY("./F114/Dry .csv", percentage= 100)
F114_mcp.MON<-mcp_analysis.POLY("./F114/Monsoon .csv", percentage= 100)
F114_mcp.PM<-mcp_analysis.POLY("./F114/Post_Monsoon .csv", percentage= 100)
F137_mcp.EM<-mcp_analysis.POLY("./F137/Emergence .csv", percentage= 100)
F137_mcp.DRY<-mcp_analysis.POLY("./F137/Dry .csv", percentage= 100)
F137_mcp.MON<-mcp_analysis.POLY("./F137/Monsoon .csv", percentage= 100)
F137_mcp.PM<-mcp_analysis.POLY("./F137/Post_Monsoon .csv", percentage= 100)
F147_mcp.EM<-mcp_analysis.POLY("./F147/Emergence .csv", percentage= 100)
F147_mcp.DRY<-mcp_analysis.POLY("./F147/Dry .csv", percentage= 100)
F147_mcp.MON<-mcp_analysis.POLY("./F147/Monsoon .csv", percentage= 100)
F147_mcp.PM<-mcp_analysis.POLY("./F147/Post_Monsoon .csv", percentage= 100)
F252_mcp.EM<-mcp_analysis.POLY("./F252/Emergence .csv", percentage= 100)
F252_mcp.DRY<-mcp_analysis.POLY("./F252/Dry .csv", percentage= 100)
F252_mcp.MON<-mcp_analysis.POLY("./F252/Monsoon .csv", percentage= 100)
F252_mcp.PM<-mcp_analysis.POLY("./F252/Post_Monsoon .csv", percentage= 100)
F36_mcp.EM<-mcp_analysis.POLY("./F36/Emergence .csv", percentage= 100)
F36_mcp.DRY<-mcp_analysis.POLY("./F36/Dry .csv", percentage= 100)
F36_mcp.MON<-mcp_analysis.POLY("./F36/Monsoon .csv", percentage= 100)
F36_mcp.PM<-mcp_analysis.POLY("./F36/Post_Monsoon .csv", percentage= 100)
F66_mcp.EM<-mcp_analysis.POLY("./F66/Emergence .csv", percentage= 100)
F66_mcp.DRY<-mcp_analysis.POLY("./F66/Dry .csv", percentage= 100)
F66_mcp.MON<-mcp_analysis.POLY("./F66/Monsoon .csv", percentage= 100)
F66_mcp.PM<-mcp_analysis.POLY("./F66/Post_Monsoon .csv", percentage= 100)
## Fortify mcp polygons for ggplot2 *SEASON*:
M215_mcp.EMT <- fortify(M215_mcp.EM, region = "id")
M215_mcp.DRYT <- fortify(M215_mcp.DRY, region = "id")
M215_mcp.MONT <- fortify(M215_mcp.MON, region = "id")
M112_mcp.DRYT <- fortify(M112_mcp.DRY, region = "id")
M112_mcp.MONT <- fortify(M112_mcp.MON, region = "id")
M112_mcp.PMT <- fortify(M112_mcp.PM, region = "id")
M119_mcp.DRYT <- fortify(M119_mcp.DRY, region = "id")
M119_mcp.MONT <- fortify(M119_mcp.MON, region = "id")
M119_mcp.PMT <- fortify(M119_mcp.PM, region = "id")
F114_mcp.EMT <- fortify(F114_mcp.EM, region = "id")
F114_mcp.DRYT <- fortify(F114_mcp.DRY, region = "id")
F114_mcp.MONT <- fortify(F114_mcp.MON, region = "id")
F114_mcp.PMT <- fortify(F114_mcp.PM, region = "id")
F137_mcp.EMT <- fortify(F137_mcp.EM, region = "id")
F137_mcp.DRYT <- fortify(F137_mcp.DRY, region = "id")
F137_mcp.MONT <- fortify(F137_mcp.MON, region = "id")
F137_mcp.PMT <- fortify(F137_mcp.PM, region = "id")
F147_mcp.EMT <- fortify(F147_mcp.EM, region = "id")
F147_mcp.DRYT <- fortify(F147_mcp.DRY, region = "id")
F147_mcp.MONT <- fortify(F147_mcp.MON, region = "id")
F147_mcp.PMT <- fortify(F147_mcp.PM, region = "id")
F252_mcp.EMT <- fortify(F252_mcp.EM, region = "id")
F252_mcp.DRYT <- fortify(F252_mcp.DRY, region = "id")
F252_mcp.MONT <- fortify(F252_mcp.MON, region = "id")
F252_mcp.PMT <- fortify(F252_mcp.PM, region = "id")
F36_mcp.EMT <- fortify(F36_mcp.EM, region = "id")
F36_mcp.DRYT <- fortify(F36_mcp.DRY, region = "id")
F36_mcp.MONT <- fortify(F36_mcp.MON, region = "id")
F36_mcp.PMT <- fortify(F36_mcp.PM, region = "id")
F66_mcp.EMT <- fortify(F66_mcp.EM, region = "id")
F66_mcp.DRYT <- fortify(F66_mcp.DRY, region = "id")
F66_mcp.MONT <- fortify(F66_mcp.MON, region = "id")
F66_mcp.PMT <- fortify(F66_mcp.PM, region = "id")
mcp.shift.TEST5 <- ggplot() +
geom_polygon(data=F114_mcp.EMT, aes(x=F114_mcp.EMT$long, y=F114_mcp.EMT$lat),
alpha=0.1,colour="blue",linetype=2) +
geom_polygon(data=F114_mcp.DRYT, aes(x=F114_mcp.DRYT$long, y=F114_mcp.DRYT$lat),
alpha=0.1,colour="red",linetype=3) +
geom_polygon(data=F114_mcp.MONT, aes(x=F114_mcp.MONT$long, y=F114_mcp.MONT$lat),
alpha=0.1,colour="green",linetype=4) +
geom_polygon(data=F114_mcp.PMT, aes(x=F114_mcp.PMT$long, y=F114_mcp.PMT$lat),
alpha=0.1,colour="black",linetype=5) +
geom_polygon(data=F137_mcp.EMT, aes(x=F137_mcp.EMT$long, y=F137_mcp.EMT$lat),
alpha=0.1,colour="blue",linetype=2) +
geom_polygon(data=F137_mcp.DRYT, aes(x=F137_mcp.DRYT$long, y=F137_mcp.DRYT$lat),
alpha=0.1,colour="red",linetype=3) +
geom_polygon(data=F137_mcp.MONT, aes(x=F137_mcp.MONT$long, y=F137_mcp.MONT$lat),
alpha=0.1,colour="green",linetype=4) +
geom_polygon(data=F137_mcp.PMT, aes(x=F137_mcp.PMT$long, y=F137_mcp.PMT$lat),
alpha=0.1,colour="black",linetype=5) +
geom_polygon(data=F147_mcp.EMT, aes(x=F147_mcp.EMT$long, y=F147_mcp.EMT$lat),
alpha=0.1,colour="blue",linetype=2) +
geom_polygon(data=F147_mcp.DRYT, aes(x=F147_mcp.DRYT$long, y=F147_mcp.DRYT$lat),
alpha=0.1,colour="red",linetype=3) +
geom_polygon(data=F147_mcp.MONT, aes(x=F147_mcp.MONT$long, y=F147_mcp.MONT$lat),
alpha=0.1,colour="green",linetype=4) +
geom_polygon(data=F147_mcp.PMT, aes(x=F147_mcp.PMT$long, y=F147_mcp.PMT$lat),
alpha=0.1,colour="black",linetype=5) +
# geom_polygon(data=F252_mcp.EMT, aes(x=F252_mcp.EMT$long, y=F252_mcp.EMT$lat),
# alpha=0.1,colour="black",linetype=2) +
# geom_polygon(data=F252_mcp.DRYT, aes(x=F252_mcp.DRYT$long, y=F252_mcp.DRYT$lat),
# alpha=0.1,colour="black",linetype=3) +
# geom_polygon(data=F252_mcp.MONT, aes(x=F252_mcp.MONT$long, y=F252_mcp.MONT$lat),
# alpha=0.1,colour="black",linetype=4) +
# geom_polygon(data=F252_mcp.PMT, aes(x=F252_mcp.PMT$long, y=F252_mcp.PMT$lat),
# alpha=0.1,colour="black",linetype=5) +
geom_polygon(data=F36_mcp.EMT, aes(x=F36_mcp.EMT$long, y=F36_mcp.EMT$lat),
alpha=0.1,colour="blue",linetype=2) +
geom_polygon(data=F36_mcp.DRYT, aes(x=F36_mcp.DRYT$long, y=F36_mcp.DRYT$lat),
alpha=0.1,colour="red",linetype=3) +
geom_polygon(data=F36_mcp.MONT, aes(x=F36_mcp.MONT$long, y=F36_mcp.MONT$lat),
alpha=0.1,colour="green",linetype=4) +
geom_polygon(data=F36_mcp.PMT, aes(x=F36_mcp.PMT$long, y=F36_mcp.PMT$lat),
alpha=0.1,colour="black",linetype=5) +
geom_polygon(data=F66_mcp.EMT, aes(x=F66_mcp.EMT$long, y=F66_mcp.EMT$lat),
alpha=0.1,colour="blue",linetype=2) +
geom_polygon(data=F66_mcp.DRYT, aes(x=F66_mcp.DRYT$long, y=F66_mcp.DRYT$lat),
alpha=0.1,colour="red",linetype=3) +
geom_polygon(data=F66_mcp.MONT, aes(x=F66_mcp.MONT$long, y=F66_mcp.MONT$lat),
alpha=0.1,colour="green",linetype=4) +
geom_polygon(data=F66_mcp.PMT, aes(x=F66_mcp.PMT$long, y=F66_mcp.PMT$lat),
alpha=0.1,colour="black",linetype=5) +
theme_bw() +
labs(x="Easting (m)", y="Northing (m)") +
labs(caption = "Figure 6 | SC seasonal home range shifts of five lizards. All seasonal polygons stay relatively stable with \n considerable overlap and without any major shifts.")+
theme(plot.caption = element_text(hjust = 0,lineheight = 0.9))+
theme(legend.position="none", plot.title = element_text(face = "bold", hjust = 0.5))
mcp.shift.TEST5
TABLE group means of seasonal home ranges between the two populations averaged across sex
seasonal<-read.csv("SC_Seasonal_Data.csv")
library(Rmisc)
SEAS_GRP_Means <- summarySE(seasonal, measurevar="Home_Range_100mcp",
groupvars=c("Environment","Season"), na.rm = TRUE)
# SEAS_GRP_Means
kable(SEAS_GRP_Means, format = "pandoc", caption = 'Table 6 | Group means of seasonal home ranges between Stone Canyon (subsidized) and Owl Head Buttes (non-subsidized). These means are averaged across sex.')
RM-ANOVA for seasonal home ranges between environments
library(lme4)
library(readr)
library(lmerTest)
# seasonal<-read.csv("SC_Seasonal_Data.csv")
RM.mod.Season <- lmer(Home_Range_100mcp~Environment+Season+Sex+N+Environment*Season+(1|Gila),
data=seasonal)
summary(RM.mod.Season)
ANOVA table of seasonal HRs between envs.
anova(RM.mod.Season)
TABLE of seasonal home ranges by sex between the two populations
SEAS_GRP_TEST <- summarySE(seasonal, measurevar="Home_Range_100mcp",
groupvars=c("Environment","Season","Sex"), na.rm = TRUE)
# SEAS_GRP_Means
kable(SEAS_GRP_TEST, format = "pandoc", caption = 'Table 7 | Seasonal home range means between Stone Canyon (subsidized) and Owl Head Buttes (non-subsidized) popuations for males and females. These are raw means before being adjusted for environment, season, sex, and sample size.')
figures for raw seasonal home ranges between the two populations

Figures Adjusted EMMs of seasonal home range between the two populations

Collective grid of raw and adjusted seasonal home ranges
ggarrange(raw.seasonal, adj.seasonal, labels = c("A", "B"),
nrow = 2)

Post hoc analyses of seasonal home ranges
Pairwise of each season between populations, overaged over levels of sex
emm_s.t <- emmeans(RM.mod.Season, pairwise ~ Environment | Season)
emm_s.t
Graphical comparisons
plot(emm_s.t, comparisons = TRUE)
Pairwise between seasons within each popultion
emm_s.t4 <- emmeans(RM.mod.Season, pairwise ~ Season | Environment)
emm_s.t4
Graphical Comps
plot(emm_s.t4, comparisons = TRUE)
Pairwise between sexes of each season of the subsidized population
sub <- subset(seasonal, Environment == "subsidized")
RM.mod.Sub <- lmer(Home_Range_100mcp~Season+Sex+N+Season*Sex+(1|Gila), data=sub)
emm_s.t5 <- emmeans(RM.mod.Sub, pairwise ~ Sex | Season)
emm_s.t5
Graphical Comps
plot(emm_s.t5, comparisons = TRUE)
Pairwise between sexes of each season of the non-subsidized population
nonsub <- subset(seasonal, Environment == "nonsubsidized")
View(nonsub)
RM.mod.NSub <- lmer(Home_Range_100mcp~Season+Sex+N+Season*Sex+(1|Gila), data=nonsub)
emm_s.t6 <- emmeans(RM.mod.NSub, pairwise ~ Sex | Season)
emm_s.t6
Graphical Comps
plot(emm_s.t6, comparisons = TRUE)
---
title: "Spatial Scripts"
output: html_notebook
---



```{r}
packrat::init("~/Desktop/Heloderma Spatial/Heloderma Spatial")
```



```{r}
# required packages
library(adehabitatHR) #for home range calculations
library(data.table) #manipulate S3 and S4 data tables
library(ggplot2) #for graphic output
library(ggfortify) #to allow ggplot2 to read spatial data
library(grid) #to add annotations to the output
# library(OpenStreetMap) #for obtaining raster images
library(pbapply) #needed for progress bar
library(plotly) #for interactive xy plot
library(rgdal) #for converting spatial data
library(sp) #for converting spatial data
library(rgeos)
# library(raster)
library(mapview)

```





Overall individual yearly home ranges for non/subsidized populations
```{r echo=FALSE}
GM_table <- read_csv("GM_table.csv")
kable(GM_table,format="pandoc", caption='Table 1 | Pooled overall home ranges of Gila Monsters at Owl Head Buttes and Stone Canyon Golf Club. Both 100% and 95% MCPs were calculated between both populations.')
```





Gila monster locations for all tracked lizards across Stone Canyon
```{r}
All.Gilas <- read_csv("./GM_Final_Data.csv")

utm_points <- cbind(All.Gilas$EASTING, All.Gilas$NORTHING)

utm_locations <- SpatialPoints(utm_points, proj4string=CRS.SC)

proj_lat.lon <- as.data.frame(spTransform(utm_locations, CRS("+proj=longlat +datum=WGS84")))
colnames(proj_lat.lon) <- c("x","y")

## FORTIGY SPATIAL SPATIAL POINTS FOR PLOTTING:
proj_lat.lon <- fortify(proj_lat.lon, region = "Type")

myMap <- get_stamenmap(bbox = c(left = -111.009,
                                bottom = 32.459,
                                right = -110.969,
                                top = 32.474),
                       maptype = "terrain", 
                       crop = FALSE,
                       zoom = 15)

ggmap(myMap)+geom_point(data=proj_lat.lon, aes(x=x, y=y), size=0.3)
```





###################### OVERALL YEARLY ANALYSES ####################


Plot of 100%  MCP HRs against number of relocations
```{r}
year <- read_csv("GM_Consolidated_ByYear.csv")

# quick plot
# Graph1<-ggplot(year,aes(x=N100,y=Home_Range_100mcp,group=Environment))+
Graph1<-ggplot(year,aes(x=N100,y=Home_Range_100mcp))+
  geom_point(aes(shape = factor(Environment)), size = 2)+
  geom_smooth(aes(linetype=Environment),colour="black", method="lm") +
  # scale_colour_manual(values=c(subsidized="cyan3",nonsubsidized="indian red1"))+
  # labs(title = "100% MCP Home Ranges")+
  xlab("Number of Relocations")+
  ylab("100% MCP Area (ha)")+
  # labs(caption = "Figure 3 | Non-Subsidized (Owl Head Buttes) vs. Subsidized (Stone Canyon) population 100% MCPs against number \n of fixes of the complete data set.")+
  theme(plot.caption = element_text(hjust = 0,lineheight = 0.9))
  # theme_bw()

Graph1<-Graph1+theme(axis.title=element_text(size = 14))

# legend at top-left, inside the plot
SCOH.hr.fig<-Graph1 + theme(legend.title = element_blank(),
               legend.text = element_text(size = 12),
               legend.justification=c(0,1),
               legend.position=c(0.05, 0.95),
               legend.background = element_blank(),
               legend.key = element_blank(),
               legend.box.background = element_rect(colour = "black")) +
               scale_shape_discrete(name  ="",
                          breaks=c("nonsubsidized", "subsidized"),
                          labels=c("Nonsubsidized", "Subsidized")) +
                            scale_linetype_discrete(name  ="",
                          breaks=c("nonsubsidized", "subsidized"),
                          labels=c("Nonsubsidized", "Subsidized"))

SCOH.hr.fig
# dir.create("outputs") # create a new folder to hold the output files
# ggsave("outputs/SC_OHB_plot.pdf")
```





Overall combined 100% MCP means averaged across sex
```{r}
library(Rmisc)
Means <- summarySE(year, measurevar="Home_Range_100mcp",
                          groupvars=c("Environment"),na.rm = TRUE)

kable(Means, format = "pandoc", caption = 'Overall combined 100% MCP means averaged across sex')
```





Overall combined 95% MCP means averaged across sex
```{r}
Means.95mcp <- summarySE(year, measurevar="Home_Range_95mcp",
                          groupvars=c("Environment"),na.rm = TRUE)
Means.95mcp
```





Set projection for mapping
```{r}
CRS.SC<-CRS("+proj=utm +zone=12 +ellps=WGS84 +units=m +no_defs")
```





Function for MCP analysis
```{r eval=FALSE, include=FALSE}
mcp_analysis <- function(filename, percentage){
  data <- read.csv(file = filename)
  x <- as.data.frame(data$EASTING)
  y <- as.data.frame(data$NORTHING)
  xy <- c(x,y)
  data.proj <- SpatialPointsDataFrame(xy,data, proj4string = CRS.SC)
  xy <- SpatialPoints(data.proj@coords)
  mcp.out <- mcp(xy, percentage, unout="ha")
  area <- as.data.frame(round(mcp.out@data$area,4))
  .rowNamesDF(area, make.names=TRUE) <- data$YEAR
  write.table(area,file="MCP_Hectares.csv",
              append=TRUE,sep=",", col.names=FALSE, row.names=TRUE)
  mcp.points <- cbind((data.frame(xy)),data$YEAR)
  colnames(mcp.points) <- c("x","y", "year")
  mcp.poly <- fortify(mcp.out, region = "id")
  units <- grid.text(paste(round(mcp.out@data$area,2)," ha"), x=0.9,  y=0.95,
                     gp=gpar(fontface=4, cex=0.9), draw = FALSE)
  mcp.plot <- ggplot() +
    geom_polygon(data=mcp.poly, aes(x=mcp.poly$long, y=mcp.poly$lat), alpha=0.5) +
    geom_point(data=mcp.points, aes(x=x, y=y)) + theme_bw() +
    labs(x="Easting (m)", y="Northing (m)", title=mcp.points$year) +
    theme(legend.position="none", plot.title = element_text(face = "bold", hjust = 0.5)) +
    annotation_custom(units)
  mcp.plot
}
```


Function of MCP polygons used for mapping
```{r eval=FALSE, include=FALSE}
# CRS.SC<-CRS("+proj=utm +zone=12 +ellps=WGS84 +units=m +no_defs")

mcp_analysis.POLY <- function(filename, percentage){
  data <- read.csv(file = filename,stringsAsFactors = FALSE)
  data.sp <- data[, c("LIZARDNUMBER", "EASTING", "NORTHING")]
  coordinates(data.sp) <- c("EASTING", "NORTHING")
  proj4string(data.sp) <- CRS.SC
  mcp_out <- mcp(data.sp, percentage, unout="ha")
}
```


Function of KDE analysis
```{r}
kde_analysis.href.plot <- function(filename, percentage){
  data <- read.csv(file = filename)
  x <- as.data.frame(data$EASTING)
  y <- as.data.frame(data$NORTHING)
  xy <- c(x,y)
  data.proj <- SpatialPointsDataFrame(xy,data, proj4string = CRS.SC)
  xy <- SpatialPoints(data.proj@coords)
  kde<-kernelUD(xy, h="href", kern="bivnorm", grid=1000)
  ver <- getverticeshr(kde, percentage)
  area <- as.data.frame(round(ver$area,4))
  .rowNamesDF(area, make.names=TRUE) <- data$LIZARDNUMBER
  write.table(area,file="KDE_Hectares.csv",
              append=TRUE,sep=",", col.names=FALSE, row.names=TRUE)
  kde.points <- cbind((data.frame(data.proj@coords)),data$LIZARDNUMBER)
  colnames(kde.points) <- c("x","y","lizardnumber")
  kde.poly <- fortify(ver, region = "id")
  units <- grid.text(paste(round(ver$area,2)," ha"), x=0.9,  y=0.95,
                     gp=gpar(fontface=4, cex=0.9), draw = FALSE)
  kde.plot <- ggplot() +
    geom_polygon(data=kde.poly, aes(x=kde.poly$long, y=kde.poly$lat), alpha = 0.5) +
    geom_point(data=kde.points, aes(x=x, y=y)) + theme_bw() +
    labs(x="Easting (m)", y="Northing (m)", title=kde.points$lizardnumber) +
    theme(legend.position="none", plot.title = element_text(face = "bold", hjust = 0.5)) +
    annotation_custom(units)
  kde.plot
}
```


Function of KDE polygons for mapping
```{r}
kde_analysis.href.polygon <- function(filename, percentage){
  data <- read.csv(file = filename)
  x <- as.data.frame(data$EASTING)
  y <- as.data.frame(data$NORTHING)
  xy <- c(x,y)
  data.proj <- SpatialPointsDataFrame(xy,data, proj4string = CRS.SC)
  xy <- SpatialPoints(data.proj@coords)
  kde<-kernelUD(xy, h="href", kern="bivnorm", grid=1000)
  ver <- getverticeshr(kde, percentage)
  ver@proj4string<-CRS.SC
  area <- as.data.frame(round(ver$area,4))
  .rowNamesDF(area, make.names=TRUE) <- data$YEAR
  write.table(area,file="KDE_Hectares.csv",
              append=TRUE,sep=",", col.names=FALSE, row.names=TRUE)
  kde.points <- cbind((data.frame(data.proj@coords)),data$YEAR)
  colnames(kde.points) <- c("x","y","year")
  kde.poly <- fortify(ver, region = "id")
  units <- grid.text(paste(round(ver$area,2)," ha"), x=0.9,  y=0.95,
                     gp=gpar(fontface=4, cex=0.9), draw = FALSE)
  ver
}
```


Function of raster of UD 
```{r}
# kde_analysis.href.raster <- function(filename){
#   data <- read.csv(file = filename)
#   x <- as.data.frame(data$EASTING)
#   y <- as.data.frame(data$NORTHING)
#   xy <- c(x,y)
#   data.proj <- SpatialPointsDataFrame(xy,data, proj4string = CRS.SC)
#   xy <- SpatialPoints(data.proj@coords)
#   kde<-kernelUD(xy, h="href", kern="bivnorm", grid=1000)
#   kde<-as(kde, "SpatialGridDataFrame")
#   kde@proj4string<- CRS.SC
#   kde
# }
```


Function of trajectory analysis and distance over time
```{r}
traj_analysis <- function(filename){
  relocs_data <- read.csv(file = filename)
  relocs <- as.ltraj(cbind(relocs_data$EASTING, relocs_data$NORTHING),id=relocs_data$LIZARDNUMBER, typeII = FALSE, date=NULL)
  relocs.df <- ld(relocs)
  relocs_dist <- as.data.frame(sum(sapply(relocs.df$dist, sum, na.rm=TRUE)))
  colnames(relocs_dist) <- "Total Distance"
  name <- relocs.df$id[1]
  row.names(relocs_dist) <- name
  relocs_units <- grid.text(paste(round(relocs_dist,2),"m"), x=0.9, y=0.9,
                            gp=gpar(fontface=3, col="black", cex=0.9), draw = FALSE)
  reloc.plot <- ggplot() + theme_classic() + geom_path(data=relocs.df, aes(x=x,y=y), linetype = "dashed", colour = "red",
                                                       arrow = arrow(length=unit(.5,"cm"), angle = 20, ends="last", type = "closed")) +
    geom_point(data=relocs.df, aes(x=x, y=y)) + geom_point(data=relocs.df, aes(x=x[1],
                                                                               y=y[1]), size = 3, color = "darkgreen", pch=0) +
    labs(x="Easting (m)", y="Northing (m)", title=relocs.df$id[1]) +
    theme(legend.position="none", plot.title = element_text(face = "bold", hjust = 0.5)) +
    annotation_custom(relocs_units)
  reloc.plot
}
```


Function of distance of time
```{r}
dist_analysis <- function(filename){
  relocs_data <- read.csv(file = filename)
  relocs <- as.ltraj(cbind(relocs_data$EASTING, relocs_data$NORTHING),id=relocs_data$LIZARDNUMBER, typeII = FALSE, date=NULL)
  relocs.df <- ld(relocs)
  relocs_dist <- as.data.frame(sum(sapply(relocs.df$dist, sum, na.rm=TRUE)))
  colnames(relocs_dist) <- "Total Distance"
  name <- relocs.df$id[1]
  row.names(relocs_dist) <- name
  write.table(relocs_dist,file="reloc_dist.csv",
              append=TRUE,sep=",", col.names=FALSE, row.names=TRUE)
  dist.plot
}
```


Map of yearly HR shifts of a subset of Gila Monsters. Includes running MCP polygons, Fortify mcp polygons for ggplot2 by YEAR
```{r}
M215_mcp.11<-mcp_analysis.POLY("./M215/2011 .csv", percentage= 100)
M215_mcp.12<-mcp_analysis.POLY("./M215/2012 .csv", percentage= 100)
F104_mcp.08<-mcp_analysis.POLY("./F104/2008 .csv", percentage= 100)
F104_mcp.09<-mcp_analysis.POLY("./F104/2009 .csv", percentage= 100)
F114_mcp.08<-mcp_analysis.POLY("./F114/2008 .csv", percentage= 100)
F114_mcp.09<-mcp_analysis.POLY("./F114/2009 .csv", percentage= 100)
F114_mcp.10<-mcp_analysis.POLY("./F114/2010 .csv", percentage= 100)
F114_mcp.11<-mcp_analysis.POLY("./F114/2011 .csv", percentage= 100)
F114_mcp.12<-mcp_analysis.POLY("./F114/2012 .csv", percentage= 100)
F137_mcp.09<-mcp_analysis.POLY("./F137/2009 .csv", percentage= 100)
F137_mcp.10<-mcp_analysis.POLY("./F137/2010 .csv", percentage= 100)
F137_mcp.11<-mcp_analysis.POLY("./F137/2011 .csv", percentage= 100)
F147_mcp.09<-mcp_analysis.POLY("./F147/2009 .csv", percentage= 100)
F147_mcp.10<-mcp_analysis.POLY("./F147/2010 .csv", percentage= 100)
F147_mcp.11<-mcp_analysis.POLY("./F147/2011 .csv", percentage= 100)
F147_mcp.12<-mcp_analysis.POLY("./F147/2012 .csv", percentage= 100)
F36_mcp.08<-mcp_analysis.POLY("./F36/2008 .csv", percentage= 100)
F36_mcp.09<-mcp_analysis.POLY("./F36/2009 .csv", percentage= 100)
F36_mcp.10<-mcp_analysis.POLY("./F36/2010 .csv", percentage= 100)
F36_mcp.11<-mcp_analysis.POLY("./F36/2011 .csv", percentage= 100)
F36_mcp.12<-mcp_analysis.POLY("./F36/2012 .csv", percentage= 100)
F66_mcp.08<-mcp_analysis.POLY("./F66/2008 .csv", percentage= 100)
F66_mcp.09<-mcp_analysis.POLY("./F66/2009 .csv", percentage= 100)
F66_mcp.10<-mcp_analysis.POLY("./F66/2010 .csv", percentage= 100)
M119_mcp.08<-mcp_analysis.POLY("./M119/2008 .csv", percentage= 100)
M119_mcp.09<-mcp_analysis.POLY("./M119/2009 .csv", percentage= 100)
M119_mcp.10<-mcp_analysis.POLY("./M119/2010 .csv", percentage= 100)
M112_mcp.07<-mcp_analysis.POLY("./M112/2007 .csv", percentage= 100)
M112_mcp.09<-mcp_analysis.POLY("./M112/2009 .csv", percentage= 100)
M112_mcp.10<-mcp_analysis.POLY("./M112/2010 .csv", percentage= 100)
M69_mcp.09<-mcp_analysis.POLY("./M69/2009 .csv", percentage= 100)
M69_mcp.10<-mcp_analysis.POLY("./M69/2010 .csv", percentage= 100)

## Fortify mcp polygons for ggplot2 *YEAR*:

F104_mcp.08T <- fortify(F104_mcp.08, region = "id")
F104_mcp.09T <- fortify(F104_mcp.09, region = "id")
F114_mcp.08T <- fortify(F114_mcp.08, region = "id")
F114_mcp.09T <- fortify(F114_mcp.09, region = "id")
F114_mcp.10T <- fortify(F114_mcp.10, region = "id")
F114_mcp.11T <- fortify(F114_mcp.11, region = "id")
F114_mcp.12T <- fortify(F114_mcp.12, region = "id")
F137_mcp.09T <- fortify(F137_mcp.09, region = "id")
F137_mcp.10T <- fortify(F137_mcp.10, region = "id")
F137_mcp.11T <- fortify(F137_mcp.11, region = "id")
F147_mcp.09T <- fortify(F147_mcp.09, region = "id")
F147_mcp.10T <- fortify(F147_mcp.10, region = "id")
F147_mcp.11T <- fortify(F147_mcp.11, region = "id")
F147_mcp.12T <- fortify(F147_mcp.12, region = "id")
F36_mcp.08T <- fortify(F36_mcp.08, region = "id")
F36_mcp.09T <- fortify(F36_mcp.09, region = "id")
F36_mcp.10T <- fortify(F36_mcp.10, region = "id")
F36_mcp.11T <- fortify(F36_mcp.11, region = "id")
F36_mcp.12T <- fortify(F36_mcp.12, region = "id")
F66_mcp.08T <- fortify(F66_mcp.08, region = "id")
F66_mcp.09T <- fortify(F66_mcp.09, region = "id")
F66_mcp.10T <- fortify(F66_mcp.10, region = "id")
M119_mcp.08T <- fortify(M119_mcp.08, region = "id")
M119_mcp.09T <- fortify(M119_mcp.09, region = "id")
M119_mcp.10T <- fortify(M119_mcp.10, region = "id")
M112_mcp.07T <- fortify(M112_mcp.07, region = "id")
M112_mcp.09T <- fortify(M112_mcp.09, region = "id")
M112_mcp.10T <- fortify(M112_mcp.10, region = "id")
M69_mcp.09T <- fortify(M69_mcp.09, region = "id")
M69_mcp.10T <- fortify(M69_mcp.10, region = "id")
M215_mcp.11T <- fortify(M215_mcp.11, region = "id")
M215_mcp.12T <- fortify(M215_mcp.12, region = "id")


mcp.shift.TEST4 <- ggplot() +
  # geom_polygon(data=F104_mcp.08T, aes(x=F104_mcp.08T$long, y=F104_mcp.08T$lat),
  #              alpha=0.1,colour="black",linetype=2) +
  # geom_polygon(data=F104_mcp.09T, aes(x=F104_mcp.09T$long, y=F104_mcp.09T$lat),
  #              alpha=0.1,colour="black",linetype=2) +
  geom_polygon(data=F114_mcp.08T, aes(x=F114_mcp.08T$long, y=F114_mcp.08T$lat),
               alpha=0.1,colour="black",linetype=3) +
  geom_polygon(data=F114_mcp.09T, aes(x=F114_mcp.09T$long, y=F114_mcp.09T$lat),
               alpha=0.1,colour="black",linetype=3) +
  geom_polygon(data=F114_mcp.10T, aes(x=F114_mcp.10T$long, y=F114_mcp.10T$lat),
               alpha=0.1,colour="black",linetype=3) +
  geom_polygon(data=F114_mcp.11T, aes(x=F114_mcp.11T$long, y=F114_mcp.11T$lat),
               alpha=0.1,colour="black",linetype=3) +
  geom_polygon(data=F114_mcp.12T, aes(x=F114_mcp.12T$long, y=F114_mcp.12T$lat),
               alpha=0.1,colour="black",linetype=3) +
  geom_polygon(data=F137_mcp.09T, aes(x=F137_mcp.09T$long, y=F137_mcp.09T$lat),
               alpha=0.1,colour="brown",linetype=4) +
  geom_polygon(data=F137_mcp.10T, aes(x=F137_mcp.10T$long, y=F137_mcp.10T$lat),
               alpha=0.1,colour="brown",linetype=4) +
  geom_polygon(data=F137_mcp.11T, aes(x=F137_mcp.11T$long, y=F137_mcp.11T$lat),
               alpha=0.1,colour="brown",linetype=4) +
  geom_polygon(data=F147_mcp.09T, aes(x=F147_mcp.09T$long, y=F147_mcp.09T$lat),
               alpha=0.1,colour="red",linetype=1) +
  geom_polygon(data=F147_mcp.10T, aes(x=F147_mcp.10T$long, y=F147_mcp.10T$lat),
               alpha=0.1,colour="red",linetype=1) +
  geom_polygon(data=F147_mcp.11T, aes(x=F147_mcp.11T$long, y=F147_mcp.11T$lat),
               alpha=0.1,colour="red",linetype=1) +
  geom_polygon(data=F147_mcp.12T, aes(x=F147_mcp.12T$long, y=F147_mcp.12T$lat),
               alpha=0.1,colour="red",linetype=1) +
  # geom_polygon(data=F36_mcp.08T, aes(x=F36_mcp.08T$long, y=F36_mcp.08T$lat),
  #              alpha=0.1,colour="black",linetype=6) +
  # geom_polygon(data=F36_mcp.09T, aes(x=F36_mcp.09T$long, y=F36_mcp.09T$lat),
  #              alpha=0.1,colour="black",linetype=6) +
  # geom_polygon(data=F36_mcp.10T, aes(x=F36_mcp.10T$long, y=F36_mcp.10T$lat),
  #              alpha=0.1,colour="black",linetype=6) +
  # geom_polygon(data=F36_mcp.11T, aes(x=F36_mcp.11T$long, y=F36_mcp.11T$lat),
  #              alpha=0.1,colour="black",linetype=6) +
  # geom_polygon(data=F36_mcp.12T, aes(x=F36_mcp.12T$long, y=F36_mcp.12T$lat),
  #              alpha=0.1,colour="black",linetype=6) +
  geom_polygon(data=F66_mcp.08T, aes(x=F66_mcp.08T$long, y=F66_mcp.08T$lat),
               alpha=0.1,colour="green",linetype=5) +
  geom_polygon(data=F66_mcp.09T, aes(x=F66_mcp.09T$long, y=F66_mcp.09T$lat),
               alpha=0.1,colour="green",linetype=5) +
  geom_polygon(data=F66_mcp.10T, aes(x=F66_mcp.10T$long, y=F66_mcp.10T$lat),
               alpha=0.1,colour="green",linetype=5) +
  geom_polygon(data=M119_mcp.08T, aes(x=M119_mcp.08T$long, y=M119_mcp.08T$lat),
               alpha=0.1,colour="blue",linetype=6) +
  geom_polygon(data=M119_mcp.09T, aes(x=M119_mcp.09T$long, y=M119_mcp.09T$lat),
               alpha=0.1,colour="blue",linetype=6) +
  geom_polygon(data=M119_mcp.10T, aes(x=M119_mcp.10T$long, y=M119_mcp.10T$lat),
               alpha=0.1,colour="blue",linetype=6) +
  geom_polygon(data=M112_mcp.07T, aes(x=M112_mcp.07T$long, y=M112_mcp.07T$lat),
               alpha=0.1,colour="purple",linetype=2) +
  geom_polygon(data=M112_mcp.09T, aes(x=M112_mcp.09T$long, y=M112_mcp.09T$lat),
               alpha=0.1,colour="purple",linetype=2) +
  geom_polygon(data=M112_mcp.10T, aes(x=M112_mcp.10T$long, y=M112_mcp.10T$lat),
               alpha=0.1,colour="purple",linetype=2) +
  # geom_polygon(data=M69_mcp.09T, aes(x=M69_mcp.09T$long, y=M69_mcp.09T$lat),
  #              alpha=0.1,colour="black") +
  # geom_polygon(data=M69_mcp.10T, aes(x=M69_mcp.10T$long, y=M69_mcp.10T$lat),
  #              alpha=0.1,colour="black") +
  # geom_polygon(data=M215_mcp.11T, aes(x=M215_mcp.11T$long, y=M215_mcp.11T$lat),
  #              alpha=0.1,colour="black") +
  # geom_polygon(data=M215_mcp.12T, aes(x=M215_mcp.12T$long, y=M215_mcp.12T$lat),
  #              alpha=0.1,colour="black") +
  theme_bw() +labs(x="Easting (m)", y="Northing (m)") +
  labs(caption = "Figure 5  |  SC Yearly home range shifts of 8 lizards, both males and females. Home range shifts appear to be \n relativley stable over study years.")+
  theme(plot.caption = element_text(hjust = 0,lineheight = 0.9))
  # theme(legend.position="none", plot.title = element_text(face = "bold", hjust = 0.5))
## within each geom_polygon line?:
## aes(colour="red"or"M112_mcp.09T")...+scale_color_manual(name="",breaks=c("","",...""))+
## values=c(""="",...)

mcp.shift.TEST4
```





Raw group 100% MCP home range means of Stone Canyon and Owl Head Buttes. Grouped by environment and sex
```{r}
library(Rmisc)
YR_GRP_Means <- summarySE(year, measurevar="Home_Range_100mcp",
                          groupvars=c("Environment","Sex"),na.rm = TRUE)

kable(YR_GRP_Means, format = "pandoc", 
      caption = 'Table 1 | Raw group 100% MCP home range means of Stone Canyon and Owl Head Buttes. Grouped by environment and sex.')
```




Raw group 95% MCP home range means of Stone Canyon and Owl Head Buttes. Grouped by environment and sex
```{r}
YR_GRP_Means95 <- summarySE(year, measurevar="Home_Range_95mcp",
                            groupvars=c("Environment","Sex"),na.rm = TRUE)

kable(YR_GRP_Means95, format = "pandoc", caption = 'Table 2 | Raw group 95% MCP home range means of raw data of Stone Canyon and Owl Head Buttes. Grouped by environment and sex.')
```




RM-ANOVA for 100% MCP analyses between the subsidized and non-subsidized
```{r}
# Get p-values from mixed model F values:
library(lme4)
library(readr)
year <- read_csv("GM_Consolidated_ByYear.csv")

RMmod.year<-lmer(Home_Range_100mcp~Environment+Year+Sex+N100+Environment*Sex+
                   (1|Gila),data = year)
summary(RMmod.year)
```


ANOVA table for 100% MCPs between the two populations
```{r}
anova(RMmod.year)
```





RM-ANOVA for 95% MCP analyses between the subsidized and non-subsidized
```{r}
RMmod.year95<-lmer(Home_Range_95mcp~Environment+Year+Sex+N100+Environment*Sex+
                   (1|Gila),data = year)
summary(RMmod.year95)
```


ANOVA table for 95% MCPs between the two populations
```{r}
anova(RMmod.year95)
```





Raw group means AND adjusted EMMs of Yearly Overall 100%MCP
```{r}
RMmod.year100<-lmer(Home_Range_100mcp~Environment+Year+Sex+N100+Environment*Sex+(1|Gila),data = year)

############################# EMMs adjusted #############################

RM.marginal <- lsmeans(RMmod.year100, 
                    ~ Environment)

## CATAGORIZE LSM GRAPH BY SEX BETWEEN ENVIRONMENT:
refRM_sex <- lsmeans(RMmod.year100, specs = c("Environment","Sex"))

# refRM_sex
ref_dfRM_sex <- as.data.frame(summary(refRM_sex))
pd_RM <- position_dodge(0.1)

yr.mean.adj<-ggplot(ref_dfRM_sex, aes(x=Sex,y=lsmean,group=Environment))+
  geom_point(aes(shape = factor(Environment)), size = 4,position=position_dodge(.1), 
            show.legend = FALSE)+
  scale_shape_manual(values=c(1, 2))+
  geom_errorbar(aes(ymin=lsmean-SE, ymax=lsmean+SE), width=.1,position=position_dodge())+
  geom_line(position=pd_RM) +
  theme_bw()  +
  xlab("") +
  ylab("") +
   theme(legend.position = c(.87,.85), legend.background = element_rect(colour = "black"),
        axis.text.x=element_blank(),
        axis.text.y  = element_text(vjust=0.5, size=8),
        axis.title.y  = element_text(size=16),
        axis.title.x  = element_text(size=10),
        legend.text = element_text(size = 12, face = "bold"),
        axis.ticks.x=element_blank(),
        strip.text = element_text(size=12)) 

yr.mean.adj

############################# Raw Group Means ############################
# pd_RM <- position_dodge(0.1)

Raw.YearHR<-ggplot(YR_GRP_Means, aes(x=Sex,y=Home_Range_100mcp,group=Environment))+
  geom_point(aes(shape = factor(Environment)), size = 4,position=position_dodge(.1))+
  geom_errorbar(aes(ymin=Home_Range_100mcp-se, ymax=Home_Range_100mcp+se),
                width=.1,position=position_dodge())+
  geom_line(position=pd_RM) +
  theme_bw()+
  xlab("")+
  ylab("100% MCP Area (ha)") +
   theme(legend.position = c(.87,.85), legend.background = element_rect(colour = "black"),
        axis.text.x=element_blank(),
        axis.text.y  = element_text(vjust=0.5, size=8),
        axis.title.y  = element_text(size=16),
        axis.title.x  = element_text(size=10),
        legend.text = element_text(size = 12, face = "bold"),
        axis.ticks.x=element_blank(),
        strip.text = element_text(size=12)) 


Raw.YearHR<-Raw.YearHR + theme(legend.title = element_blank(),
                     legend.text = element_text(size = 12),
                     legend.justification=c(0,1),
                     legend.position=c(0.05, 0.95),
                     legend.background = element_blank(),
                     legend.key = element_blank(),
                     legend.box.background = element_rect(colour = "black")) +
   scale_shape_discrete(name  ="",
                          breaks=c("nonsubsidized", "subsidized"),
                          labels=c("Nonsubsidized", "Subsidized"))

Raw.YearHR

library(ggpubr)
library(gridExtra)
library(grid)

# grid.arrange(Raw.YearHR, yr.mean.adj, nrow = 1,  
#              bottom = textGrob("Figure 5 | a. Raw group means of overall yearly home ranges between males and females. Note that the male \n home range of the subsidized population is smaller than that of the female home range in the non-subsidized \n population. b. Group means of home ranges after being adjusted for environment, year, sex, and sample size.",
#                                gp = gpar(fontface = 1,fontsize = 10),hjust = 0, x = 0))

# grid.arrange(Raw.YearHR, yr.mean.adj, nrow = 1)
# grid.arrange(Raw.YearHR, yr.mean.adj, nrow = 1)
# ggarrange(Raw.YearHR, yr.mean.adj, labels = c("A", "B"),
#           ncol = 2)
```






Directional means of home range (100% MCP) after being adjusted for year, sex and sample size
```{r}
kable(ref_dfRM_sex, format = "pandoc", caption = 'Table | Directional means of home range (100% MCP) after being adjusted for year, sex and sample size.')
```




RM-ANOVA of 95% KDEs for the subsidized population
```{r}
RM.KDEmod.year<-lmer(Home_Range_95kde~Year+Sex+N+(1|Gila),data = sub)

summary(RM.KDEmod.year)
```

ANOVA Table for 95%KDE
```{r}
anova(RM.KDEmod.year)
```




RM-ANOVA of 50% KDEs for the subsidized
```{r}
RM.KDE.50.mod.year<-lmer(Home_Range_50kde~Year+Sex+N+(1|Gila),data = sub)

summary(RM.KDE.50.mod.year)
```

ANOVA Talbe of 50%  KDE for the subsidized
```{r}
anova(RM.KDE.50.mod.year)
```




TABLE. Raw Group 50% KDE home range means male and female home ranges at Stone Canyon
```{r}
YR_GRP_Means.50KDE <- summarySE(sub, measurevar="Home_Range_50kde",
                            groupvars=c("Sex"),na.rm = TRUE)

kable(YR_GRP_Means.50KDE, format = "pandoc", caption = 'Table 5 | Raw Group 50% KDE home range means male and female home ranges at Stone Canyon.')
```




Raw group means AND adjusted EMMs of Yearly Overall 95% KDEs between non/subsidized populations
```{r}
RMmod.95kde<-lmer(Home_Range_95kde~Environment+Year+Sex+N+Environment*Sex+(1|Gila),data = year)

############################ EMMs of 95% KDEs ###########################

RM.marginal <- lsmeans(RMmod.95kde, 
                       ~ Environment)

## CATAGORIZE LSM GRAPH BY SEX BETWEEN ENVIRONMENT:
refRM_kde <- lsmeans(RMmod.95kde, specs = c("Environment","Sex"))

# refRM_sex
ref_dfRM_kde <- as.data.frame(summary(refRM_kde))
# pd_RM <- position_dodge(0.1)

kde.mean.adj<-ggplot(ref_dfRM_kde, aes(x=Sex,y=lsmean,group=Environment))+
  geom_point(aes(shape = factor(Environment)), size = 4,position=position_dodge(.1), 
             show.legend = FALSE)+
  scale_shape_manual(values=c(1, 2))+
  geom_errorbar(aes(ymin=lsmean-SE, ymax=lsmean+SE), width=.1,position=position_dodge())+
  geom_line(position=pd_RM) +
  theme_bw()+
  xlab("")+
  ylab("")+
  theme(legend.position = c(.87,.85), legend.background = element_rect(colour = "black"),
        axis.text.x  = element_text(vjust=0.5, size=16),
        axis.text.y  = element_text(vjust=0.5, size=8),
        axis.title.y  = element_text(size=16),
        axis.title.x  = element_text(size=10),
        legend.text = element_text(size = 12, face = "bold"),
        strip.text = element_blank()) +
  scale_x_discrete(labels=c("Female", "Male")) 

kde.mean.adj

############################ raw EMMs of 95% KDEs ###########################

# pd_RM <- position_dodge(0.1)

# geom_line(position=pd)+

Raw.kde<-ggplot(YR_Means.95KDEall, aes(x=Sex,y=Home_Range_95kde,group=Environment)) +
  geom_point(aes(shape = factor(Environment)), size = 4,position=position_dodge(.1),
             show.legend = FALSE) +
  geom_errorbar(aes(ymin=Home_Range_95kde-se, ymax=Home_Range_95kde+se),
                width=.1, position=position_dodge()) +
  geom_line(position=pd_RM) +
  theme_bw() +
  xlab("") +
  ylab("95% KDE Area (ha)") +
  theme(legend.position = c(.87,.85), legend.background = element_rect(colour = "black"),
        axis.text.x  = element_text(vjust=0.5, size=16),
        axis.text.y  = element_text(vjust=0.5, size=8),
        axis.title.y  = element_text(size=16),
        axis.title.x  = element_text(size=10),
        legend.text = element_text(size = 12, face = "bold"),
        strip.text = element_blank()) +
  scale_x_discrete(labels=c("Female", "Male")) 

Raw.kde

# Raw.kde<-Raw.kde + theme(legend.title = element_blank(),
#                                legend.text = element_text(size = 12),
#                                legend.justification=c(0,1),
#                                legend.position=c(0.05, 0.95),
#                                legend.background = element_blank(),
#                                legend.key = element_blank(),
#                                legend.box.background = element_rect(colour = "black")) +
#   scale_shape_discrete(name  ="",
#                        breaks=c("nonsubsidized", "subsidized"),
#                        labels=c("Nonsubsidized", "Subsidized"))
# Raw.kde

library(gridExtra)
library(grid)

# ggarrange(Raw.kde, kde.mean.adj, labels = c("A", "B"),
#           nrow = 1)

```


Collective grid of 100% MCP and 95% KDE of both sites from above
```{r}
ggarrange(Raw.YearHR, yr.mean.adj, Raw.kde, kde.mean.adj, labels = c("A", "B", "C","D"),
          ncol = 2, nrow = 2)
```




43.4 male 42.9 female
Yearly overall means of 95% KDEs grouped by site and sex
```{r}
YR_Means.95KDEall <- summarySE(year, measurevar="Home_Range_95kde",
                            groupvars=c("Environment","Sex"),na.rm = TRUE)
 
kable(YR_Means.95KDEall, format = "pandoc", caption = 'Table | Raw Group 95% KDE home range means male and female home ranges at non/subsidized.')
```





Pairwise Comparisons, between sexes by environment, and between environments averaged across sex
```{r}
RMmod.year.Em<-lmer(Home_Range_100mcp~Environment+Year+Sex+N100+Environment*Sex+
                      (1|Gila),data = year)

# RMmod.year.Em95<-lmer(Home_Range_95mcp~Environment+Year+Sex+N95+Environment*Sex+
#                       (1|Gila),data = year)

# Sex.emm.oa <- emmeans(RMmod.year.Em, c("Environment","Sex"))
# pairs(Sex.emm.oa)

emm_s.t2 <- emmeans(RMmod.year.Em, pairwise ~ Sex | Environment)
emm_s.t2
# emm_s.e1 <- emmeans(RMmod.year.Em, pairwise ~ Environment)
# emm_s.e1
```


Graphical Comparisons of Sex Within Each Environment:
```{r}
plot(emm_s.t2, comparisons = TRUE, xlab = "Least Square Mean (ha)", ylab = "Environment")
```




Pairwise by sex between enviornements 100%  MCP, and 95% KDEs
```{r}
emm_s.t3 <- emmeans(RMmod.year.Em, pairwise ~ Environment | Sex)
emm_s.t3

# emm_s.t95 <- emmeans(RMmod.year.Em95, pairwise ~ Environment | Sex)
# emm_s.t95
```



Graphical Comparisons of Sex between the two populations:
```{r}
plot(emm_s.t3, comparisons = TRUE, xlab = "Least Square Mean (ha)", ylab = "Environment")
```





Ineractive map of MCPs at Stone Canyon
```{r}
M67_MCP<-mcp_analysis.POLY('./M67/M67 .csv', percentage= 100)
M69_MCP<-mcp_analysis.POLY('./M69/M69 .csv', percentage= 100)
M255_MCP<-mcp_analysis.POLY('./M255/M255 .csv', percentage= 100)
M215_MCP<-mcp_analysis.POLY('./M215/M215 .csv', percentage= 100)
M14_MCP<-mcp_analysis.POLY('./M14/M14 .csv', percentage= 100)
M119_MCP<-mcp_analysis.POLY('./M119/M119 .csv', percentage= 100)
M112_MCP<-mcp_analysis.POLY('./M112/M112 .csv', percentage= 100)

F66_MCP<-mcp_analysis.POLY('./F66/F66 .csv', percentage= 100)
F36_MCP<-mcp_analysis.POLY('./F36/F36 .csv', percentage= 100)
F252_MCP<-mcp_analysis.POLY('./F252/F252 .csv', percentage= 100)
F214_MCP<-mcp_analysis.POLY('./F214/F214 .csv', percentage= 100)
F200_MCP<-mcp_analysis.POLY('./F200/F200 .csv', percentage= 100)
F147_MCP<-mcp_analysis.POLY('./F147/F147 .csv', percentage= 100)
F146_MCP<-mcp_analysis.POLY('./F146/F146 .csv', percentage= 100)
F137_MCP<-mcp_analysis.POLY('./F137/F137 .csv', percentage= 100)
F135_MCP<-mcp_analysis.POLY('./F135/F135 .csv', percentage= 100)
F114_MCP<-mcp_analysis.POLY('./F114/F114 .csv', percentage= 100)
F104_MCP<-mcp_analysis.POLY('./F104/F104 .csv', percentage= 100)

Male.MCP <- rbind(M67_MCP,M69_MCP,M255_MCP,M215_MCP,M14_MCP,M119_MCP,M112_MCP)
Female.MCP <- rbind(F66_MCP,F36_MCP,F252_MCP,F214_MCP,F200_MCP,F147_MCP,F146_MCP,F137_MCP,
                    F135_MCP,F114_MCP,F104_MCP)

mapviewOptions(basemaps = c("OpenStreetMap","Esri.WorldImagery","OpenTopoMap"),
               na.color = "magenta",
               layers.control.pos = "topleft")

mapview(Male.MCP, legend=F, zcol="id", col.regions = c("blue"), alpha.regions=0.3) + 
  mapview(Female.MCP, legend=F, zcol = "id", col.regions = c("red"), alpha.regions=0.3)
```





Create stagnant stamen map of MCPs at Stone Canyon
```{r echo=FALSE, message=FALSE, warning=FALSE, paged.print=FALSE}
## Get/view the stamen map (bbox should be adjusted appropriately):
myMap <- get_stamenmap(bbox = c(left = -111.009,
                                bottom = 32.459,
                                right = -110.969,
                                top = 32.474),
                       maptype = "terrain", 
                       crop = FALSE,
                       zoom = 15)

F104_latlon <- spTransform(F104_MCP, CRS("+proj=longlat +datum=WGS84"))
F114_latlon <- spTransform(F114_MCP, CRS("+proj=longlat +datum=WGS84"))
F135_latlon <- spTransform(F135_MCP, CRS("+proj=longlat +datum=WGS84"))
F137_latlon <- spTransform(F137_MCP, CRS("+proj=longlat +datum=WGS84"))
F146_latlon <- spTransform(F146_MCP, CRS("+proj=longlat +datum=WGS84"))
F147_latlon <- spTransform(F147_MCP, CRS("+proj=longlat +datum=WGS84"))
F200_latlon <- spTransform(F200_MCP, CRS("+proj=longlat +datum=WGS84"))
F214_latlon <- spTransform(F214_MCP, CRS("+proj=longlat +datum=WGS84"))
F252_latlon <- spTransform(F252_MCP, CRS("+proj=longlat +datum=WGS84"))
F36_latlon <- spTransform(F36_MCP, CRS("+proj=longlat +datum=WGS84"))
F66_latlon <- spTransform(F66_MCP, CRS("+proj=longlat +datum=WGS84"))
M112_latlon <- spTransform(M112_MCP, CRS("+proj=longlat +datum=WGS84"))
M119_latlon <- spTransform(M119_MCP, CRS("+proj=longlat +datum=WGS84"))
M14_latlon <- spTransform(M14_MCP, CRS("+proj=longlat +datum=WGS84"))
M215_latlon <- spTransform(M215_MCP, CRS("+proj=longlat +datum=WGS84"))
M255_latlon <- spTransform(M255_MCP, CRS("+proj=longlat +datum=WGS84"))
M67_latlon <- spTransform(M67_MCP, CRS("+proj=longlat +datum=WGS84"))
M69_latlon <- spTransform(M69_MCP, CRS("+proj=longlat +datum=WGS84"))

SC_stamen_map <- ggmap(myMap) +
  # geom_point(data = proj_lat.lon, aes(x=x, y=y), size = 0.3, alpha = 0.8, color = "black") +
  geom_polygon(data = fortify(F104_latlon), aes(long, lat, group=group), colour = "red", 
             fill = NA) +
  geom_polygon(data = fortify(F114_latlon), aes(long, lat, group=group), colour = "red", 
               fill = NA) +
  geom_polygon(data = fortify(F135_latlon), aes(long, lat, group=group), colour = "red", 
               fill = NA) +
  geom_polygon(data = fortify(F137_latlon), aes(long, lat, group=group), colour = "red", 
               fill = NA) +
  geom_polygon(data = fortify(F146_latlon), aes(long, lat, group=group), colour = "red", 
               fill = NA) +
  geom_polygon(data = fortify(F147_latlon), aes(long, lat, group=group), colour = "red", 
               fill = NA) +
  geom_polygon(data = fortify(F200_latlon), aes(long, lat, group=group), colour = "red", 
               fill = NA) +
  geom_polygon(data = fortify(F214_latlon), aes(long, lat, group=group), colour = "red", 
               fill = NA) +
  geom_polygon(data = fortify(F252_latlon), aes(long, lat, group=group), colour = "red", 
               fill = NA) +
  geom_polygon(data = fortify(F36_latlon), aes(long, lat, group=group), colour = "red", 
               fill = NA) +
  geom_polygon(data = fortify(F66_latlon), aes(long, lat, group=group), colour = "red", 
               fill = NA) +
  geom_polygon(data = fortify(M112_latlon), aes(long, lat, group=group), colour = "blue", 
               fill = NA) +
  geom_polygon(data = fortify(M119_latlon), aes(long, lat, group=group), colour = "blue", 
               fill = NA) +
  geom_polygon(data = fortify(M14_latlon), aes(long, lat, group=group), colour = "blue", 
               fill = NA) +
  geom_polygon(data = fortify(M215_latlon), aes(long, lat, group=group), colour = "blue", 
               fill = NA) +
  geom_polygon(data = fortify(M255_latlon), aes(long, lat, group=group), colour = "blue", 
               fill = NA) +
  geom_polygon(data = fortify(M67_latlon), aes(long, lat, group=group), colour = "blue", 
               fill = NA) +
  geom_polygon(data = fortify(M69_latlon), aes(long, lat, group=group), colour = "blue", 
               fill = NA) +
  xlab("Longitude") +
  ylab("Latitude") 

# SC_stamen_map

library(ggsn)

SC_stamen_map<-SC_stamen_map + ggsn::scalebar(x.min = -110.972, x.max = -110.966,
                     y.min = 32.474, y.max = 32.476, 
                     dist = 500, dist_unit="m", 
                     height=0.19,
                     st.bottom=TRUE, 
                     st.dist=0.3,
                     st.size=3.5,
                     transform = TRUE, 
                     model = 'WGS84') 
# SC_stamen_map
SC_stamen_map+north2(SC_stamen_map, x = 0.89, y = 0.85, scale = 0.1, symbol = 16)
```




Interactive map of KDEs at Stone Canyon
```{r}
M67_KDE<-kde_analysis.href.polygon('./M67/M67 .csv', percentage= 95)
M69_KDE<-kde_analysis.href.polygon('./M69/M69 .csv', percentage= 95)
M255_KDE<-kde_analysis.href.polygon('./M255/M255 .csv', percentage= 95)
M215_KDE<-kde_analysis.href.polygon('./M215/M215 .csv', percentage= 95)
M14_KDE<-kde_analysis.href.polygon('./M14/M14 .csv', percentage= 95)
M119_KDE<-kde_analysis.href.polygon('./M119/M119 .csv', percentage= 95)
M112_KDE<-kde_analysis.href.polygon('./M112/M112 .csv', percentage= 95)

F66_KDE<-kde_analysis.href.polygon('./F66/F66 .csv', percentage= 95)
F36_KDE<-kde_analysis.href.polygon('./F36/F36 .csv', percentage= 95)
F252_KDE<-kde_analysis.href.polygon('./F252/F252 .csv', percentage= 95)
F214_KDE<-kde_analysis.href.polygon('./F214/F214 .csv', percentage= 95)
F200_KDE<-kde_analysis.href.polygon('./F200/F200 .csv', percentage= 95)
F147_KDE<-kde_analysis.href.polygon('./F147/F147 .csv', percentage= 95)
F146_KDE<-kde_analysis.href.polygon('./F146/F146 .csv', percentage= 95)
F137_KDE<-kde_analysis.href.polygon('./F137/F137 .csv', percentage= 95)
F135_KDE<-kde_analysis.href.polygon('./F135/F135 .csv', percentage= 95)
F114_KDE<-kde_analysis.href.polygon('./F114/F114 .csv', percentage= 95)
F104_KDE<-kde_analysis.href.polygon('./F104/F104 .csv', percentage= 95)

Male.KDE <- rbind(M67_KDE,M69_KDE,M255_KDE,M215_KDE,M14_KDE,M119_KDE,M112_KDE)
Female.KDE <- rbind(F66_KDE,F36_KDE,F252_KDE,F214_KDE,F200_KDE,F147_KDE,F146_KDE,F137_KDE,
                    F135_KDE,F114_KDE,F104_KDE)

mapviewOptions(basemaps = c("OpenStreetMap","Esri.WorldImagery","OpenTopoMap"),
               na.color = "magenta",
               layers.control.pos = "topleft")

mapview(Male.KDE, legend=F, zcol="id", col.regions = c("blue"), alpha.regions=0.3) + 
  mapview(Female.KDE, legend=F, zcol = "id", col.regions = c("red"), alpha.regions=0.3)
```




TABLE 
```{r}
kable(ref_dfRM_kde, format = "pandoc", caption = 'Table  | Subsidized and non-subsidized directional means of KDE home ranges after being adjusted for year, sex and sample size.')
```




############### SEASONAL ANALYSES ##################


Map of seasonal fluctions of home ranges
```{r}
## Create MCP polygons by SEASON:
M215_mcp.EM<-mcp_analysis.POLY("./M215/Emergence .csv", percentage= 100)
M215_mcp.DRY<-mcp_analysis.POLY("./M215/Dry .csv", percentage= 100)
M215_mcp.MON<-mcp_analysis.POLY("./M215/Monsoon .csv", percentage= 100)

M112_mcp.DRY<-mcp_analysis.POLY("./M112/Dry .csv", percentage= 100)
M112_mcp.MON<-mcp_analysis.POLY("./M112/Monsoon .csv", percentage= 100)
M112_mcp.PM<-mcp_analysis.POLY("./M112/Post_Monsoon .csv", percentage= 100)

M119_mcp.DRY<-mcp_analysis.POLY("./M119/Dry .csv", percentage= 100)
M119_mcp.MON<-mcp_analysis.POLY("./M119/Monsoon .csv", percentage= 100)
M119_mcp.PM<-mcp_analysis.POLY("./M119/Post_Monsoon .csv", percentage= 100)

F114_mcp.EM<-mcp_analysis.POLY("./F114/Emergence .csv", percentage= 100)
F114_mcp.DRY<-mcp_analysis.POLY("./F114/Dry .csv", percentage= 100)
F114_mcp.MON<-mcp_analysis.POLY("./F114/Monsoon .csv", percentage= 100)
F114_mcp.PM<-mcp_analysis.POLY("./F114/Post_Monsoon .csv", percentage= 100)

F137_mcp.EM<-mcp_analysis.POLY("./F137/Emergence .csv", percentage= 100)
F137_mcp.DRY<-mcp_analysis.POLY("./F137/Dry .csv", percentage= 100)
F137_mcp.MON<-mcp_analysis.POLY("./F137/Monsoon .csv", percentage= 100)
F137_mcp.PM<-mcp_analysis.POLY("./F137/Post_Monsoon .csv", percentage= 100)

F147_mcp.EM<-mcp_analysis.POLY("./F147/Emergence .csv", percentage= 100)
F147_mcp.DRY<-mcp_analysis.POLY("./F147/Dry .csv", percentage= 100)
F147_mcp.MON<-mcp_analysis.POLY("./F147/Monsoon .csv", percentage= 100)
F147_mcp.PM<-mcp_analysis.POLY("./F147/Post_Monsoon .csv", percentage= 100)

F252_mcp.EM<-mcp_analysis.POLY("./F252/Emergence .csv", percentage= 100)
F252_mcp.DRY<-mcp_analysis.POLY("./F252/Dry .csv", percentage= 100)
F252_mcp.MON<-mcp_analysis.POLY("./F252/Monsoon .csv", percentage= 100)
F252_mcp.PM<-mcp_analysis.POLY("./F252/Post_Monsoon .csv", percentage= 100)

F36_mcp.EM<-mcp_analysis.POLY("./F36/Emergence .csv", percentage= 100)
F36_mcp.DRY<-mcp_analysis.POLY("./F36/Dry .csv", percentage= 100)
F36_mcp.MON<-mcp_analysis.POLY("./F36/Monsoon .csv", percentage= 100)
F36_mcp.PM<-mcp_analysis.POLY("./F36/Post_Monsoon .csv", percentage= 100)

F66_mcp.EM<-mcp_analysis.POLY("./F66/Emergence .csv", percentage= 100)
F66_mcp.DRY<-mcp_analysis.POLY("./F66/Dry .csv", percentage= 100)
F66_mcp.MON<-mcp_analysis.POLY("./F66/Monsoon .csv", percentage= 100)
F66_mcp.PM<-mcp_analysis.POLY("./F66/Post_Monsoon .csv", percentage= 100)

## Fortify mcp polygons for ggplot2 *SEASON*:
M215_mcp.EMT <- fortify(M215_mcp.EM, region = "id")
M215_mcp.DRYT <- fortify(M215_mcp.DRY, region = "id")
M215_mcp.MONT <- fortify(M215_mcp.MON, region = "id")

M112_mcp.DRYT <- fortify(M112_mcp.DRY, region = "id")
M112_mcp.MONT <- fortify(M112_mcp.MON, region = "id")
M112_mcp.PMT <- fortify(M112_mcp.PM, region = "id")

M119_mcp.DRYT <- fortify(M119_mcp.DRY, region = "id")
M119_mcp.MONT <- fortify(M119_mcp.MON, region = "id")
M119_mcp.PMT <- fortify(M119_mcp.PM, region = "id")

F114_mcp.EMT <- fortify(F114_mcp.EM, region = "id")
F114_mcp.DRYT <- fortify(F114_mcp.DRY, region = "id")
F114_mcp.MONT <- fortify(F114_mcp.MON, region = "id")
F114_mcp.PMT <- fortify(F114_mcp.PM, region = "id")

F137_mcp.EMT <- fortify(F137_mcp.EM, region = "id")
F137_mcp.DRYT <- fortify(F137_mcp.DRY, region = "id")
F137_mcp.MONT <- fortify(F137_mcp.MON, region = "id")
F137_mcp.PMT <- fortify(F137_mcp.PM, region = "id")

F147_mcp.EMT <- fortify(F147_mcp.EM, region = "id")
F147_mcp.DRYT <- fortify(F147_mcp.DRY, region = "id")
F147_mcp.MONT <- fortify(F147_mcp.MON, region = "id")
F147_mcp.PMT <- fortify(F147_mcp.PM, region = "id")

F252_mcp.EMT <- fortify(F252_mcp.EM, region = "id")
F252_mcp.DRYT <- fortify(F252_mcp.DRY, region = "id")
F252_mcp.MONT <- fortify(F252_mcp.MON, region = "id")
F252_mcp.PMT <- fortify(F252_mcp.PM, region = "id")

F36_mcp.EMT <- fortify(F36_mcp.EM, region = "id")
F36_mcp.DRYT <- fortify(F36_mcp.DRY, region = "id")
F36_mcp.MONT <- fortify(F36_mcp.MON, region = "id")
F36_mcp.PMT <- fortify(F36_mcp.PM, region = "id")

F66_mcp.EMT <- fortify(F66_mcp.EM, region = "id")
F66_mcp.DRYT <- fortify(F66_mcp.DRY, region = "id")
F66_mcp.MONT <- fortify(F66_mcp.MON, region = "id")
F66_mcp.PMT <- fortify(F66_mcp.PM, region = "id")

mcp.shift.TEST5 <- ggplot() +
  geom_polygon(data=F114_mcp.EMT, aes(x=F114_mcp.EMT$long, y=F114_mcp.EMT$lat),
               alpha=0.1,colour="blue",linetype=2) +
  geom_polygon(data=F114_mcp.DRYT, aes(x=F114_mcp.DRYT$long, y=F114_mcp.DRYT$lat),
               alpha=0.1,colour="red",linetype=3) +
  geom_polygon(data=F114_mcp.MONT, aes(x=F114_mcp.MONT$long, y=F114_mcp.MONT$lat),
               alpha=0.1,colour="green",linetype=4) +
  geom_polygon(data=F114_mcp.PMT, aes(x=F114_mcp.PMT$long, y=F114_mcp.PMT$lat),
               alpha=0.1,colour="black",linetype=5) +
  geom_polygon(data=F137_mcp.EMT, aes(x=F137_mcp.EMT$long, y=F137_mcp.EMT$lat),
               alpha=0.1,colour="blue",linetype=2) +
  geom_polygon(data=F137_mcp.DRYT, aes(x=F137_mcp.DRYT$long, y=F137_mcp.DRYT$lat),
               alpha=0.1,colour="red",linetype=3) +
  geom_polygon(data=F137_mcp.MONT, aes(x=F137_mcp.MONT$long, y=F137_mcp.MONT$lat),
               alpha=0.1,colour="green",linetype=4) +
  geom_polygon(data=F137_mcp.PMT, aes(x=F137_mcp.PMT$long, y=F137_mcp.PMT$lat),
               alpha=0.1,colour="black",linetype=5) +
  geom_polygon(data=F147_mcp.EMT, aes(x=F147_mcp.EMT$long, y=F147_mcp.EMT$lat),
               alpha=0.1,colour="blue",linetype=2) +
  geom_polygon(data=F147_mcp.DRYT, aes(x=F147_mcp.DRYT$long, y=F147_mcp.DRYT$lat),
               alpha=0.1,colour="red",linetype=3) +
  geom_polygon(data=F147_mcp.MONT, aes(x=F147_mcp.MONT$long, y=F147_mcp.MONT$lat),
               alpha=0.1,colour="green",linetype=4) +
  geom_polygon(data=F147_mcp.PMT, aes(x=F147_mcp.PMT$long, y=F147_mcp.PMT$lat),
               alpha=0.1,colour="black",linetype=5) +
  # geom_polygon(data=F252_mcp.EMT, aes(x=F252_mcp.EMT$long, y=F252_mcp.EMT$lat),
  #              alpha=0.1,colour="black",linetype=2) +
  # geom_polygon(data=F252_mcp.DRYT, aes(x=F252_mcp.DRYT$long, y=F252_mcp.DRYT$lat),
  #              alpha=0.1,colour="black",linetype=3) +
  # geom_polygon(data=F252_mcp.MONT, aes(x=F252_mcp.MONT$long, y=F252_mcp.MONT$lat),
  #              alpha=0.1,colour="black",linetype=4) +
  # geom_polygon(data=F252_mcp.PMT, aes(x=F252_mcp.PMT$long, y=F252_mcp.PMT$lat),
  #              alpha=0.1,colour="black",linetype=5) +
  geom_polygon(data=F36_mcp.EMT, aes(x=F36_mcp.EMT$long, y=F36_mcp.EMT$lat),
               alpha=0.1,colour="blue",linetype=2) +
  geom_polygon(data=F36_mcp.DRYT, aes(x=F36_mcp.DRYT$long, y=F36_mcp.DRYT$lat),
               alpha=0.1,colour="red",linetype=3) +
  geom_polygon(data=F36_mcp.MONT, aes(x=F36_mcp.MONT$long, y=F36_mcp.MONT$lat),
               alpha=0.1,colour="green",linetype=4) +
  geom_polygon(data=F36_mcp.PMT, aes(x=F36_mcp.PMT$long, y=F36_mcp.PMT$lat),
               alpha=0.1,colour="black",linetype=5) +
  geom_polygon(data=F66_mcp.EMT, aes(x=F66_mcp.EMT$long, y=F66_mcp.EMT$lat),
               alpha=0.1,colour="blue",linetype=2) +
  geom_polygon(data=F66_mcp.DRYT, aes(x=F66_mcp.DRYT$long, y=F66_mcp.DRYT$lat),
               alpha=0.1,colour="red",linetype=3) +
  geom_polygon(data=F66_mcp.MONT, aes(x=F66_mcp.MONT$long, y=F66_mcp.MONT$lat),
               alpha=0.1,colour="green",linetype=4) +
  geom_polygon(data=F66_mcp.PMT, aes(x=F66_mcp.PMT$long, y=F66_mcp.PMT$lat),
               alpha=0.1,colour="black",linetype=5) +
  theme_bw() +
  labs(x="Easting (m)", y="Northing (m)") +
  labs(caption = "Figure 6 |  SC seasonal home range shifts of five lizards. All seasonal polygons stay relatively stable with \n considerable overlap and without any major shifts.")+
  theme(plot.caption = element_text(hjust = 0,lineheight = 0.9))+
  theme(legend.position="none", plot.title = element_text(face = "bold", hjust = 0.5))

mcp.shift.TEST5
```




TABLE group means  of seasonal home ranges between the  two  populations averaged across sex
```{r}
seasonal<-read.csv("SC_Seasonal_Data.csv")

library(Rmisc)

SEAS_GRP_Means <- summarySE(seasonal, measurevar="Home_Range_100mcp",
                            groupvars=c("Environment","Season"), na.rm = TRUE)

# SEAS_GRP_Means
kable(SEAS_GRP_Means, format = "pandoc", caption = 'Table 6 | Group means of seasonal home ranges between Stone Canyon (subsidized) and Owl Head Buttes (non-subsidized). These means are averaged across sex.')
```




RM-ANOVA for seasonal home ranges between environments
```{r}
library(lme4)
library(readr)
library(lmerTest)
# seasonal<-read.csv("SC_Seasonal_Data.csv")

RM.mod.Season <- lmer(Home_Range_100mcp~Environment+Season+Sex+N+Environment*Season+(1|Gila), 
                      data=seasonal)
summary(RM.mod.Season)
```


ANOVA table of seasonal HRs between envs.
```{r}
anova(RM.mod.Season)
```




TABLE of seasonal home ranges by sex between the two populations
```{r}
SEAS_GRP_TEST <- summarySE(seasonal, measurevar="Home_Range_100mcp",
                           groupvars=c("Environment","Season","Sex"), na.rm = TRUE)

# SEAS_GRP_Means
kable(SEAS_GRP_TEST, format = "pandoc", caption = 'Table 7 | Seasonal home range means between Stone Canyon (subsidized) and Owl Head Buttes (non-subsidized) popuations for males and females. These are raw means before being adjusted for environment, season, sex, and sample size.')
```




figures for raw seasonal home ranges between the two populations
```{r}
pd <- position_dodge(0.3) # move them .05 to the left and right ('dodges')

# relevel factor season:
SEAS_GRP_TEST$Season<-relevel(SEAS_GRP_TEST$Season,"Emergence")

# New facet label names for seasons
# season.labs <- c("Dry", "Emergence", "Monsoon", "Post Monsoon")
# names(season.labs) <- c("Dry", "Emergence", "Monsoon", "Post_Monsoon")

season.labs <- c("Emergence", "Dry", "Monsoon", "Post Monsoon")
names(season.labs) <- c("Emergence", "Dry", "Monsoon", "Post_Monsoon")

## TEST 3
raw.seasonal<-ggplot(SEAS_GRP_TEST,aes(x=Environment, y=Home_Range_100mcp, shape=Sex)) + 
  geom_point(aes(shape=Sex), size = 4, position=pd) +
  geom_errorbar(aes(ymin=Home_Range_100mcp-se, ymax=Home_Range_100mcp+se), position = pd,
                width=0.3, size=0.5, lty=1) +
  facet_grid(~Season, labeller=labeller(Season=season.labs)) +
   theme_bw() +
  theme(legend.position = c(.87,.85), legend.background = element_rect(colour = "black"),
        plot.title = element_text(lineheight=1.5, face="bold", size=rel(1.5), hjust = 0.5),
        # axis.text.x  = element_text(vjust=0.5, size=8),
        axis.text.x=element_blank(),
        axis.text.y  = element_text(vjust=0.5, size=8),
        axis.title.y  = element_text(size=16),
        axis.title.x  = element_blank(),
        legend.title = element_blank(),
        legend.text = element_text(size = 12, face = "bold"),
        axis.ticks.x=element_blank(),
        strip.text = element_text(size=12)) +
  xlab("") + ylab("100% MCP Area (ha)") 

raw.seasonal
```




Figures Adjusted EMMs of seasonal home range between the two populations
```{r}
RM.mod.Season <- lmer(Home_Range_100mcp~Environment+Season+Sex+N+Environment*Season+(1|Gila), data=seasonal)

# RM.marginal <- lsmeans(RM.mod.Season, 
#                     ~ Environment)
# RM.marginal

## CATAGORIZE LSM GRAPH BY SEX BETWEEN ENVIRONMENT:
refRM_season <- lsmeans(RM.mod.Season, specs = c("Environment","Season","Sex"))

# refRM_sex
ref_dfRM_season <- as.data.frame(summary(refRM_season))
pd_RM <- position_dodge(0.2)

# relevel factor season for graphing purposes:
ref_dfRM_season$Season<-relevel(ref_dfRM_season$Season,"Emergence")

# New facet label names for seasons
# season.labs <- c("Dry", "Emergence", "Monsoon", "Post Monsoon")
# names(season.labs) <- c("Dry", "Emergence", "Monsoon", "Post_Monsoon")

# season.labs <- c("Emergence", "Dry", "Monsoon", "Post Monsoon")
# names(season.labs) <- c("Emergence", "Dry", "Monsoon", "Post_Monsoon")

adj.seasonal<-ggplot(ref_dfRM_season,aes(x=Environment, y=lsmean, shape=Sex)) + 
  geom_point(aes(shape=Sex), size = 4, position=pd, show.legend=FALSE) +
  scale_shape_manual(values=c(1, 2)) +
  geom_errorbar(aes(ymin=lsmean-SE, ymax=lsmean+SE), position = pd,
                width=0.3, size=0.5, lty=1) + 
  facet_grid(~Season, labeller=labeller(Season=season.labs)) +
  theme_bw()+
  theme(legend.position = c(.87,.85), legend.background = element_rect(colour = "black"),
        axis.text.x  = element_text(vjust=0.5, size=16),
        axis.text.y  = element_text(vjust=0.5, size=8),
        axis.title.y  = element_text(size=16),
        axis.title.x  = element_text(size=10),
        # legend.text = element_text(size = 12, face = "bold"),
        strip.text = element_blank()) +
  scale_x_discrete(labels=c("Non", "Sub")) +
  xlab("") + ylab("100% MCP Area (ha)")

adj.seasonal
```


Collective grid of raw and adjusted seasonal home ranges
```{r}
ggarrange(raw.seasonal, adj.seasonal, labels = c("A", "B"),
          nrow = 2)
```




Post hoc analyses of seasonal home ranges

Pairwise of each season between populations, overaged over levels of sex
```{r}
emm_s.t <- emmeans(RM.mod.Season, pairwise ~ Environment | Season)
emm_s.t
```

Graphical comparisons
```{r}
plot(emm_s.t, comparisons = TRUE)
```




Pairwise between seasons within each popultion 
```{r}
emm_s.t4 <- emmeans(RM.mod.Season, pairwise ~ Season | Environment)
emm_s.t4
```

Graphical Comps
```{r}
plot(emm_s.t4, comparisons = TRUE)
```



Pairwise between sexes of each season of the  subsidized population
```{r}
sub <- subset(seasonal, Environment == "subsidized")

RM.mod.Sub <- lmer(Home_Range_100mcp~Season+Sex+N+Season*Sex+(1|Gila), data=sub)

emm_s.t5 <- emmeans(RM.mod.Sub, pairwise ~ Sex | Season)
emm_s.t5 
```

Graphical Comps
```{r}
plot(emm_s.t5, comparisons = TRUE)
```



Pairwise between sexes of each season of the  non-subsidized population
```{r}
nonsub <- subset(seasonal, Environment == "nonsubsidized")
View(nonsub)
RM.mod.NSub <- lmer(Home_Range_100mcp~Season+Sex+N+Season*Sex+(1|Gila), data=nonsub)

emm_s.t6 <- emmeans(RM.mod.NSub, pairwise ~ Sex | Season)
emm_s.t6 
```

Graphical Comps
```{r}
plot(emm_s.t6, comparisons = TRUE)
```




